2014 |
a}, Katarzyna Rz{c; Luckner, Marcin 3D model reconstruction and evaluation using a collection of points extracted from the series of photographs Inproceedings 2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014, pp. 669–677, 2014, ISBN: 9788360810583. Abstract | Links | BibTeX | Tags: 3D reconstruction, Epipolar geometry, Features extraction, Image matching, Models evaluation @inproceedings{Luckner2014bb, title = {3D model reconstruction and evaluation using a collection of points extracted from the series of photographs}, author = {Katarzyna Rz{c{a}}zewska and Marcin Luckner}, url = {https://fedcsis.org/proceedings/2014/drp/304.html}, doi = {10.15439/2014F304}, isbn = {9788360810583}, year = {2014}, date = {2014-09-01}, booktitle = {2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014}, pages = {669--677}, abstract = {This work describes the whole process of 3D model reconstruction. It begins with the representation of the method that is used to find the matching between photographs and the methodology to use the data to form the initial structure of the reconstructed model, represented by a point cloud. As a next stage, a refinement process is performed, using the bundle adjustment method. A set of stereovision methods is used later on to find a more detailed solution. Those algorithms use pairs of images, therefore as a prerequisite a set of routines that aggregates those results is studied. The paper is concluded with a description of how the point cloud is processed, including the surface reconstruction, to form the result. The described methodology is illustrated with reconstructions of three series of professional photographs from a public repository and one series of amateur photographs created especially for this work. The results were evaluated by the proposed area matching and contour matching measures.}, keywords = {3D reconstruction, Epipolar geometry, Features extraction, Image matching, Models evaluation}, pubstate = {published}, tppubtype = {inproceedings} } This work describes the whole process of 3D model reconstruction. It begins with the representation of the method that is used to find the matching between photographs and the methodology to use the data to form the initial structure of the reconstructed model, represented by a point cloud. As a next stage, a refinement process is performed, using the bundle adjustment method. A set of stereovision methods is used later on to find a more detailed solution. Those algorithms use pairs of images, therefore as a prerequisite a set of routines that aggregates those results is studied. The paper is concluded with a description of how the point cloud is processed, including the surface reconstruction, to form the result. The described methodology is illustrated with reconstructions of three series of professional photographs from a public repository and one series of amateur photographs created especially for this work. The results were evaluated by the proposed area matching and contour matching measures. |
Luckner, Marcin; ł, Micha; ł, Pawe Stable web spam detection using features based on lexical items Journal Article Computers & Security, 46 , pp. 79–93, 2014, ISSN: 01674048. Abstract | Links | BibTeX | Tags: Context analysis, Lexical items analysis, Regular expressions, Spam detection features, Web spam detection @article{Luckner2014ab, title = {Stable web spam detection using features based on lexical items}, author = {Marcin Luckner and Micha{ł} Michal Gad and Pawe{ł} Pawel Sobkowiak}, url = {http://dx.doi.org/10.1016/j.cose.2014.07.006 http://linkinghub.elsevier.com/retrieve/pii/S0167404814001151}, doi = {10.1016/j.cose.2014.07.006}, issn = {01674048}, year = {2014}, date = {2014-01-01}, journal = {Computers & Security}, volume = {46}, pages = {79--93}, abstract = {Web spam is a method of manipulating search engines results by improving ranks of spam pages. It takes various forms and lacks a consistent definition. Web spam detectors use machine learning techniques to detect spam. However, the detectors are mostly verified on data sets coming from the same year as the learning sets. In this paper we compared Support Vector Machine classifiers trained and tested on WEBSPAM–UK data sets from different years. To obtain stable results we proposed new lexical-based features. The HTML document – transformed into a text without HTML tags, a set of visible symbols, and a list of links including the ones from tags – gave information about weird combinations of letters; consonant clusters; statistics on syllables, words, and sentences; and the Gunning Fog Index. Using data collected in 2006 as a learning set, we obtained very stable accuracy among years. This choice of the training set reduced the sensitivity in 2007, but that can be improved by managing the acceptance threshold. Finally, we proved that the balance between the sensitivity and the specificity measured by the Area Under the Curve (AUC) is improved by our selection of features.}, keywords = {Context analysis, Lexical items analysis, Regular expressions, Spam detection features, Web spam detection}, pubstate = {published}, tppubtype = {article} } Web spam is a method of manipulating search engines results by improving ranks of spam pages. It takes various forms and lacks a consistent definition. Web spam detectors use machine learning techniques to detect spam. However, the detectors are mostly verified on data sets coming from the same year as the learning sets. In this paper we compared Support Vector Machine classifiers trained and tested on WEBSPAM–UK data sets from different years. To obtain stable results we proposed new lexical-based features. The HTML document – transformed into a text without HTML tags, a set of visible symbols, and a list of links including the ones from tags – gave information about weird combinations of letters; consonant clusters; statistics on syllables, words, and sentences; and the Gunning Fog Index. Using data collected in 2006 as a learning set, we obtained very stable accuracy among years. This choice of the training set reduced the sensitivity in 2007, but that can be improved by managing the acceptance threshold. Finally, we proved that the balance between the sensitivity and the specificity measured by the Area Under the Curve (AUC) is improved by our selection of features. |
Filasiak, Robert; Grzenda, Maciej; Luckner, Marcin; Zawistowski, Pawel On the testing of network cyber threat detection methods on spam example Journal Article Annales des Telecommunications/Annals of Telecommunications, 69 (7-8), pp. 363–377, 2014, ISSN: 19589395. Abstract | Links | BibTeX | Tags: Flow analysis, Network data sets, Network Intrusion Detection Systems (NIDS), Spam detection @article{Filasiak2014b, title = {On the testing of network cyber threat detection methods on spam example}, author = {Robert Filasiak and Maciej Grzenda and Marcin Luckner and Pawel Zawistowski}, url = {http://dx.doi.org/10.1007/s12243-013-0412-5}, doi = {10.1007/s12243-013-0412-5}, issn = {19589395}, year = {2014}, date = {2014-01-01}, journal = {Annales des Telecommunications/Annals of Telecommunications}, volume = {69}, number = {7-8}, pages = {363--377}, publisher = {Springer Paris}, abstract = {As a response to the increasing number of cyber threats, novel detection and prevention methods are constantly being developed. One of the main obstacles hindering the development and evaluation of such methods is the shortage of reference data sets. What is proposed in this work is a way of testing methods detecting network threats. It includes a procedure for creating realistic reference data sets describing network threats and the processing and use of these data sets in testing environments. The proposed approach is illustrated and validated on the basis of the problem of spam detection. Reference data sets for spam detection are developed, analysed and used to both generate the requested volume of simulated traffic and analyse it using machine learning algorithms. The tests take into account both the accuracy and performance of threat detection methods under real load and constrained computing resources. textcopyright 2014 The Author(s).}, keywords = {Flow analysis, Network data sets, Network Intrusion Detection Systems (NIDS), Spam detection}, pubstate = {published}, tppubtype = {article} } As a response to the increasing number of cyber threats, novel detection and prevention methods are constantly being developed. One of the main obstacles hindering the development and evaluation of such methods is the shortage of reference data sets. What is proposed in this work is a way of testing methods detecting network threats. It includes a procedure for creating realistic reference data sets describing network threats and the processing and use of these data sets in testing environments. The proposed approach is illustrated and validated on the basis of the problem of spam detection. Reference data sets for spam detection are developed, analysed and used to both generate the requested volume of simulated traffic and analyse it using machine learning algorithms. The tests take into account both the accuracy and performance of threat detection methods under real load and constrained computing resources. textcopyright 2014 The Author(s). |
Luckner, Marcin Global and local rejection option in multi-classification task Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 483–490, 2014, ISSN: 16113349. Abstract | Links | BibTeX | Tags: Graph Ensemble, Pattern recognition, Rejection Option, Support Vector Machines @inproceedings{Luckner2014e, title = {Global and local rejection option in multi-classification task}, author = {Marcin Luckner}, doi = {10.1007/978-3-319-11179-7_61}, issn = {16113349}, year = {2014}, date = {2014-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8681 LNCS}, pages = {483--490}, abstract = {This work presents two rejection options. The global rejection option separates the foreign observations - not defined in the classification task - from the normal observations. The local rejection option works after the classification process and separates observations individually for each class. We present implementation of both methods for binary classifiers grouped in a graph structure (tree or directed acyclic graph). Next, we prove that the quality of rejection is identical for both options and depends only on the quality of binary classifiers. The methods are compared on the handwritten digits recognition task. The local rejection option works better for the most part. ? 2014 Springer International Publishing Switzerland.}, keywords = {Graph Ensemble, Pattern recognition, Rejection Option, Support Vector Machines}, pubstate = {published}, tppubtype = {inproceedings} } This work presents two rejection options. The global rejection option separates the foreign observations - not defined in the classification task - from the normal observations. The local rejection option works after the classification process and separates observations individually for each class. We present implementation of both methods for binary classifiers grouped in a graph structure (tree or directed acyclic graph). Next, we prove that the quality of rejection is identical for both options and depends only on the quality of binary classifiers. The methods are compared on the handwritten digits recognition task. The local rejection option works better for the most part. ? 2014 Springer International Publishing Switzerland. |
Homenda, Wladyslaw; Luckner, Marcin; Pedrycz, Witold Classification with rejection based on various SVM techniques Inproceedings Proceedings of the International Joint Conference on Neural Networks, pp. 3480–3487, 2014, ISBN: 9781479914845. Abstract | Links | BibTeX | Tags: @inproceedings{Homenda2014c, title = {Classification with rejection based on various SVM techniques}, author = {Wladyslaw Homenda and Marcin Luckner and Witold Pedrycz}, doi = {10.1109/IJCNN.2014.6889655}, isbn = {9781479914845}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, pages = {3480--3487}, abstract = {The task of identifying native and foreign elements and rejecting foreign ones in the pattern recognition problem is discussed in this paper. Such the task is a nonstandard aspect of pattern recognition, which is rarely present in research. In this paper, ensembles of support vector machines solving two-classes and one-class problems are employed as classification tools and as basic tools for rejecting of foreign elements. Evaluation of quality of classification and rejection methods are proposed in the paper and finally some experiments are performed in order to illustrate acquainted terms and methods.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The task of identifying native and foreign elements and rejecting foreign ones in the pattern recognition problem is discussed in this paper. Such the task is a nonstandard aspect of pattern recognition, which is rarely present in research. In this paper, ensembles of support vector machines solving two-classes and one-class problems are employed as classification tools and as basic tools for rejecting of foreign elements. Evaluation of quality of classification and rejection methods are proposed in the paper and finally some experiments are performed in order to illustrate acquainted terms and methods. |
Homenda, Wladyslaw; Luckner, Marcin; Pedrycz, Witold Classification with rejection based on various SVM techniques Inproceedings Proceedings of the International Joint Conference on Neural Networks, pp. 3480–3487, 2014, ISBN: 9781479914845. Abstract | Links | BibTeX | Tags: @inproceedings{Homenda2014, title = {Classification with rejection based on various SVM techniques}, author = {Wladyslaw Homenda and Marcin Luckner and Witold Pedrycz}, doi = {10.1109/IJCNN.2014.6889655}, isbn = {9781479914845}, year = {2014}, date = {2014-01-01}, booktitle = {Proceedings of the International Joint Conference on Neural Networks}, pages = {3480--3487}, abstract = {The task of identifying native and foreign elements and rejecting foreign ones in the pattern recognition problem is discussed in this paper. Such the task is a nonstandard aspect of pattern recognition, which is rarely present in research. In this paper, ensembles of support vector machines solving two-classes and one-class problems are employed as classification tools and as basic tools for rejecting of foreign elements. Evaluation of quality of classification and rejection methods are proposed in the paper and finally some experiments are performed in order to illustrate acquainted terms and methods.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The task of identifying native and foreign elements and rejecting foreign ones in the pattern recognition problem is discussed in this paper. Such the task is a nonstandard aspect of pattern recognition, which is rarely present in research. In this paper, ensembles of support vector machines solving two-classes and one-class problems are employed as classification tools and as basic tools for rejecting of foreign elements. Evaluation of quality of classification and rejection methods are proposed in the paper and finally some experiments are performed in order to illustrate acquainted terms and methods. |
Luckner, Marcin Global and local rejection option in multi-classification task Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 483–490, 2014, ISSN: 16113349. Abstract | Links | BibTeX | Tags: Graph Ensemble, Pattern recognition, Rejection Option, Support Vector Machines @inproceedings{Luckner2014b, title = {Global and local rejection option in multi-classification task}, author = {Marcin Luckner}, doi = {10.1007/978-3-319-11179-7_61}, issn = {16113349}, year = {2014}, date = {2014-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8681 LNCS}, pages = {483--490}, abstract = {This work presents two rejection options. The global rejection option separates the foreign observations - not defined in the classification task - from the normal observations. The local rejection option works after the classification process and separates observations individually for each class. We present implementation of both methods for binary classifiers grouped in a graph structure (tree or directed acyclic graph). Next, we prove that the quality of rejection is identical for both options and depends only on the quality of binary classifiers. The methods are compared on the handwritten digits recognition task. The local rejection option works better for the most part. ? 2014 Springer International Publishing Switzerland.}, keywords = {Graph Ensemble, Pattern recognition, Rejection Option, Support Vector Machines}, pubstate = {published}, tppubtype = {inproceedings} } This work presents two rejection options. The global rejection option separates the foreign observations - not defined in the classification task - from the normal observations. The local rejection option works after the classification process and separates observations individually for each class. We present implementation of both methods for binary classifiers grouped in a graph structure (tree or directed acyclic graph). Next, we prove that the quality of rejection is identical for both options and depends only on the quality of binary classifiers. The methods are compared on the handwritten digits recognition task. The local rejection option works better for the most part. ? 2014 Springer International Publishing Switzerland. |
Filasiak, Robert; Grzenda, Maciej; Luckner, Marcin; Zawistowski, Pawel On the testing of network cyber threat detection methods on spam example Journal Article Annales des Telecommunications/Annals of Telecommunications, 69 (7-8), pp. 363–377, 2014, ISSN: 19589395. Abstract | Links | BibTeX | Tags: Flow analysis, Network data sets, Network Intrusion Detection Systems (NIDS), Spam detection @article{Filasiak2014, title = {On the testing of network cyber threat detection methods on spam example}, author = {Robert Filasiak and Maciej Grzenda and Marcin Luckner and Pawel Zawistowski}, url = {http://dx.doi.org/10.1007/s12243-013-0412-5}, doi = {10.1007/s12243-013-0412-5}, issn = {19589395}, year = {2014}, date = {2014-01-01}, journal = {Annales des Telecommunications/Annals of Telecommunications}, volume = {69}, number = {7-8}, pages = {363--377}, publisher = {Springer Paris}, abstract = {As a response to the increasing number of cyber threats, novel detection and prevention methods are constantly being developed. One of the main obstacles hindering the development and evaluation of such methods is the shortage of reference data sets. What is proposed in this work is a way of testing methods detecting network threats. It includes a procedure for creating realistic reference data sets describing network threats and the processing and use of these data sets in testing environments. The proposed approach is illustrated and validated on the basis of the problem of spam detection. Reference data sets for spam detection are developed, analysed and used to both generate the requested volume of simulated traffic and analyse it using machine learning algorithms. The tests take into account both the accuracy and performance of threat detection methods under real load and constrained computing resources. textcopyright 2014 The Author(s).}, keywords = {Flow analysis, Network data sets, Network Intrusion Detection Systems (NIDS), Spam detection}, pubstate = {published}, tppubtype = {article} } As a response to the increasing number of cyber threats, novel detection and prevention methods are constantly being developed. One of the main obstacles hindering the development and evaluation of such methods is the shortage of reference data sets. What is proposed in this work is a way of testing methods detecting network threats. It includes a procedure for creating realistic reference data sets describing network threats and the processing and use of these data sets in testing environments. The proposed approach is illustrated and validated on the basis of the problem of spam detection. Reference data sets for spam detection are developed, analysed and used to both generate the requested volume of simulated traffic and analyse it using machine learning algorithms. The tests take into account both the accuracy and performance of threat detection methods under real load and constrained computing resources. textcopyright 2014 The Author(s). |
Luckner, Marcin; ł, Micha; ł, Pawe Stable web spam detection using features based on lexical items Journal Article Computers & Security, 46 , pp. 79–93, 2014, ISSN: 01674048. Abstract | Links | BibTeX | Tags: Context analysis, Lexical items analysis, Regular expressions, Spam detection features, Web spam detection @article{Luckner2014a, title = {Stable web spam detection using features based on lexical items}, author = {Marcin Luckner and Micha{ł} Michal Gad and Pawe{ł} Pawel Sobkowiak}, url = {http://dx.doi.org/10.1016/j.cose.2014.07.006 http://linkinghub.elsevier.com/retrieve/pii/S0167404814001151}, doi = {10.1016/j.cose.2014.07.006}, issn = {01674048}, year = {2014}, date = {2014-01-01}, journal = {Computers & Security}, volume = {46}, pages = {79--93}, abstract = {Web spam is a method of manipulating search engines results by improving ranks of spam pages. It takes various forms and lacks a consistent definition. Web spam detectors use machine learning techniques to detect spam. However, the detectors are mostly verified on data sets coming from the same year as the learning sets. In this paper we compared Support Vector Machine classifiers trained and tested on WEBSPAM–UK data sets from different years. To obtain stable results we proposed new lexical-based features. The HTML document – transformed into a text without HTML tags, a set of visible symbols, and a list of links including the ones from tags – gave information about weird combinations of letters; consonant clusters; statistics on syllables, words, and sentences; and the Gunning Fog Index. Using data collected in 2006 as a learning set, we obtained very stable accuracy among years. This choice of the training set reduced the sensitivity in 2007, but that can be improved by managing the acceptance threshold. Finally, we proved that the balance between the sensitivity and the specificity measured by the Area Under the Curve (AUC) is improved by our selection of features.}, keywords = {Context analysis, Lexical items analysis, Regular expressions, Spam detection features, Web spam detection}, pubstate = {published}, tppubtype = {article} } Web spam is a method of manipulating search engines results by improving ranks of spam pages. It takes various forms and lacks a consistent definition. Web spam detectors use machine learning techniques to detect spam. However, the detectors are mostly verified on data sets coming from the same year as the learning sets. In this paper we compared Support Vector Machine classifiers trained and tested on WEBSPAM–UK data sets from different years. To obtain stable results we proposed new lexical-based features. The HTML document – transformed into a text without HTML tags, a set of visible symbols, and a list of links including the ones from tags – gave information about weird combinations of letters; consonant clusters; statistics on syllables, words, and sentences; and the Gunning Fog Index. Using data collected in 2006 as a learning set, we obtained very stable accuracy among years. This choice of the training set reduced the sensitivity in 2007, but that can be improved by managing the acceptance threshold. Finally, we proved that the balance between the sensitivity and the specificity measured by the Area Under the Curve (AUC) is improved by our selection of features. |
2013 |
Sroka, Adrian; Luckner, Marcin Tree Symbols Detection for Green Space Estimation Incollection Advanced Concepts for Intelligent Vision Systems, Acivs 2013, pp. 526–537, 2013. @incollection{Sroka2013b, title = {Tree Symbols Detection for Green Space Estimation}, author = {Adrian Sroka and Marcin Luckner}, url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000332973500047&KeyUID=WOS:000332973500047}, year = {2013}, date = {2013-01-01}, booktitle = {Advanced Concepts for Intelligent Vision Systems, Acivs 2013}, pages = {526--537}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } |
Luckner, Marcin; Filasiak, Robert Reference data sets for spam detection: Creation, analysis, propagation Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 212–221, 2013, ISSN: 03029743. Abstract | Links | BibTeX | Tags: Anomaly detection, Flow analysis, Hybrid classifiers, Reference sets, Spam detection @inproceedings{Luckner2013f, title = {Reference data sets for spam detection: Creation, analysis, propagation}, author = {Marcin Luckner and Robert Filasiak}, doi = {10.1007/978-3-642-40846-5_22}, issn = {03029743}, year = {2013}, date = {2013-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8073 LNAI}, pages = {212--221}, abstract = {A reference set is a set of data of network traffic whose form and content allows detecting an event or a group of events. Realistic and representative datasets based on real traffic can improve research in the fields of intruders and anomaly detection. Creating reference sets tackles a number of issues such as the collection and storage of large volumes of data, the privacy of information and the relevance of collected events. Moreover, rare events are hard to analyse among background traffic and need specialist detection tools. One of the common problems that can be detected in network traffic is spam. This paper presents the methodology for creating a network traffic reference set for spam detection. The methodology concerns the selection of significant features, the collection and storage of data, the analysis of the collected data, the enrichment of the data with additional events and the propagation of the set. Moreover, a hybrid classifier that detects spam on relatively high level is presented. textcopyright 2013 Springer-Verlag.}, keywords = {Anomaly detection, Flow analysis, Hybrid classifiers, Reference sets, Spam detection}, pubstate = {published}, tppubtype = {inproceedings} } A reference set is a set of data of network traffic whose form and content allows detecting an event or a group of events. Realistic and representative datasets based on real traffic can improve research in the fields of intruders and anomaly detection. Creating reference sets tackles a number of issues such as the collection and storage of large volumes of data, the privacy of information and the relevance of collected events. Moreover, rare events are hard to analyse among background traffic and need specialist detection tools. One of the common problems that can be detected in network traffic is spam. This paper presents the methodology for creating a network traffic reference set for spam detection. The methodology concerns the selection of significant features, the collection and storage of data, the analysis of the collected data, the enrichment of the data with additional events and the propagation of the set. Moreover, a hybrid classifier that detects spam on relatively high level is presented. textcopyright 2013 Springer-Verlag. |
Luckner, Marcin; Szyszko, Krzysztof RBF ensemble based on reduction of DAG structure Inproceedings Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 99–105, IEEE, Kraków, 2013. Abstract | Links | BibTeX | Tags: Accuracy, binary classifiers, Chebyshev approximation, classes similarity, Classification, classification cost reduction, DAG structure reduction, Directed Acyclic Graph, directed graphs, Euclidean distance, Glass, Kernel, learning (artificial intelligence), pattern classification, Radial Basis Function, radial basis function ensemble, radial basis function networks, RBF ensemble, recognition accuracy, Support Vector Machines, UCI repository @inproceedings{Luckner2013ab, title = {RBF ensemble based on reduction of DAG structure}, author = {Marcin Luckner and Krzysztof Szyszko}, url = {https://fedcsis.org/proceedings/2013/pliks/334.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {Proceedings of the 2013 Federated Conference on Computer Science and Information Systems}, pages = {99--105}, publisher = {IEEE}, address = {Kraków}, abstract = {Binary classifiers are grouped into an ensemble to solve multi-class problems. One of proposed ensemble structure is a directed acyclic graph. In this structure, a classifier is created for each pair of classes. The number of classifiers can be reduced if groups of classes will be separated instead of individual classes. The proposed method is based on the similarity of classes defined as a distance between classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. In this paper, the proposed method is tested in variants based on various metrics. For the tests, several datasets from UCI repository was used and the results were compared with published works. The tests proved that grouping of radial basis functions into such ensemble reduces the classification cost and the recognition accuracy is not reduced significantly.}, keywords = {Accuracy, binary classifiers, Chebyshev approximation, classes similarity, Classification, classification cost reduction, DAG structure reduction, Directed Acyclic Graph, directed graphs, Euclidean distance, Glass, Kernel, learning (artificial intelligence), pattern classification, Radial Basis Function, radial basis function ensemble, radial basis function networks, RBF ensemble, recognition accuracy, Support Vector Machines, UCI repository}, pubstate = {published}, tppubtype = {inproceedings} } Binary classifiers are grouped into an ensemble to solve multi-class problems. One of proposed ensemble structure is a directed acyclic graph. In this structure, a classifier is created for each pair of classes. The number of classifiers can be reduced if groups of classes will be separated instead of individual classes. The proposed method is based on the similarity of classes defined as a distance between classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. In this paper, the proposed method is tested in variants based on various metrics. For the tests, several datasets from UCI repository was used and the results were compared with published works. The tests proved that grouping of radial basis functions into such ensemble reduces the classification cost and the recognition accuracy is not reduced significantly. |
Rudzinski, Jacek; Luckner, Marcin Low-cost computer vision based automatic scoring of shooting targets Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 185–195, 2013, ISSN: 03029743. Abstract | Links | BibTeX | Tags: Computer vision, Hough transform, Pattern recognition, Score estimation @inproceedings{Rudzinski2013b, title = {Low-cost computer vision based automatic scoring of shooting targets}, author = {Jacek Rudzinski and Marcin Luckner}, doi = {10.1007/978-3-642-37343-5_19}, issn = {03029743}, year = {2013}, date = {2013-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {7828 LNAI}, pages = {185--195}, abstract = {This paper introduces an automatic scoring algorithm on shooting target based on computer vision techniques. As opposed to professional solutions, proposed system requires no additional equipment and relies solely on existing straightforward image processing such as the Prewitt edge detection and the Hough transformation. Experimental results show that the method can obtain high quality scoring. The proposed algorithm detects holes with 99 percent, resulting in 92 percent after eliminating false positives. The average error on the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lower than a tournament-scoring threshold. Therefore the system can be suitable for amateur shooters interested in professional (tournament-grade) accuracy. ? 2013 Springer-Verlag.}, keywords = {Computer vision, Hough transform, Pattern recognition, Score estimation}, pubstate = {published}, tppubtype = {inproceedings} } This paper introduces an automatic scoring algorithm on shooting target based on computer vision techniques. As opposed to professional solutions, proposed system requires no additional equipment and relies solely on existing straightforward image processing such as the Prewitt edge detection and the Hough transformation. Experimental results show that the method can obtain high quality scoring. The proposed algorithm detects holes with 99 percent, resulting in 92 percent after eliminating false positives. The average error on the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lower than a tournament-scoring threshold. Therefore the system can be suitable for amateur shooters interested in professional (tournament-grade) accuracy. ? 2013 Springer-Verlag. |
Furtak, J; Grzenda, Maciej; ł, Jaros; Luckner, Marcin; Szmit, M; Afonso, J; Baghdadi, Y; Benslimane, S M; Chainbi, W; Chojnacki, A; Dabrowski, A; Davies, J; Fern?ndez, A; Frankowski, J; Fuchs, L; Furtak, J; Gaaloul, W; Garc?a-Dom?nguez, A; Garc?a-Osorio, C; Grabowski, S; Kiedrowicz, M; Korbel, P; Kowalczyk, E; Kowalski, A; Macukow, B Frontiers in network applications, network systems and web services Inproceedings 2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013, 2013, ISBN: 9781467344715. BibTeX | Tags: @inproceedings{Furtak2013b, title = {Frontiers in network applications, network systems and web services}, author = {J Furtak and Maciej Grzenda and Jaros{ł}aw Legierski and Marcin Luckner and M Szmit and J Afonso and Y Baghdadi and S M Benslimane and W Chainbi and A Chojnacki and A Dabrowski and J Davies and A Fern?ndez and J Frankowski and L Fuchs and J Furtak and W Gaaloul and A Garc?a-Dom?nguez and C Garc?a-Osorio and S Grabowski and M Kiedrowicz and P Korbel and E Kowalczyk and A Kowalski and B Macukow}, isbn = {9781467344715}, year = {2013}, date = {2013-01-01}, booktitle = {2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Luckner, Marcin; Filasiak, Robert Flow-level Spam Modelling using separate data sources Inproceedings Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, pp. 91 – 98, IEEE, 2013. Abstract | Links | BibTeX | Tags: Detection, Flow analysis, Intrusion, Network, Network data sets, Spam detection, Systems (NIDS) @inproceedings{Luckner2013bb, title = {Flow-level Spam Modelling using separate data sources}, author = {Marcin Luckner and Robert Filasiak}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6643981}, year = {2013}, date = {2013-01-01}, booktitle = {Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on}, pages = {91 -- 98}, publisher = {IEEE}, abstract = {Spam detection based on flow-level statistics is a new approach in anti-spam techniques. The approach reduces number of collected data but still can obtain relative good results in a spam detection task. The main problems in the approach are selection of flow-level features that describe spam and detection of discrimination rules. In this work, flow-level model of spam is presented. The model describes spam subclasses and brings information about major features of a spam detection task. The model is the base for decision trees that detect spam. The analysis of detectors, which was learned from data collected from different mail servers, results in the universal spam description consists of the most significant features. Flows described by selected features and collected on Broadband Remote Access Server were analysed by an ensemble of created classifiers. The ensemble detected major sources of spam among senders IP addresses.}, keywords = {Detection, Flow analysis, Intrusion, Network, Network data sets, Spam detection, Systems (NIDS)}, pubstate = {published}, tppubtype = {inproceedings} } Spam detection based on flow-level statistics is a new approach in anti-spam techniques. The approach reduces number of collected data but still can obtain relative good results in a spam detection task. The main problems in the approach are selection of flow-level features that describe spam and detection of discrimination rules. In this work, flow-level model of spam is presented. The model describes spam subclasses and brings information about major features of a spam detection task. The model is the base for decision trees that detect spam. The analysis of detectors, which was learned from data collected from different mail servers, results in the universal spam description consists of the most significant features. Flows described by selected features and collected on Broadband Remote Access Server were analysed by an ensemble of created classifiers. The ensemble detected major sources of spam among senders IP addresses. |
Homenda, Wladyslaw; Luckner, Marcin; Pedrycz, Witold Classification with rejection : concepts and formal evaluations Inproceedings 8th International Conference on Knowledge, Information and Creativity Support, pp. 161–172, Kraków, 2013, ISBN: 9781479914845. BibTeX | Tags: Binary classifiers ensemble, Reclassification, Rejection rule @inproceedings{Homenda2013b, title = {Classification with rejection : concepts and formal evaluations}, author = {Wladyslaw Homenda and Marcin Luckner and Witold Pedrycz}, isbn = {9781479914845}, year = {2013}, date = {2013-01-01}, booktitle = {8th International Conference on Knowledge, Information and Creativity Support}, pages = {161--172}, address = {Kraków}, keywords = {Binary classifiers ensemble, Reclassification, Rejection rule}, pubstate = {published}, tppubtype = {inproceedings} } |
Luckner, Marcin; Filasiak, Robert Flow-level Spam Modelling using separate data sources Inproceedings Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, pp. 91 – 98, IEEE, 2013. Abstract | Links | BibTeX | Tags: Detection, Flow analysis, Intrusion, Network, Network data sets, Spam detection, Systems (NIDS) @inproceedings{Luckner2013b, title = {Flow-level Spam Modelling using separate data sources}, author = {Marcin Luckner and Robert Filasiak}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6643981}, year = {2013}, date = {2013-01-01}, booktitle = {Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on}, pages = {91 -- 98}, publisher = {IEEE}, abstract = {Spam detection based on flow-level statistics is a new approach in anti-spam techniques. The approach reduces number of collected data but still can obtain relative good results in a spam detection task. The main problems in the approach are selection of flow-level features that describe spam and detection of discrimination rules. In this work, flow-level model of spam is presented. The model describes spam subclasses and brings information about major features of a spam detection task. The model is the base for decision trees that detect spam. The analysis of detectors, which was learned from data collected from different mail servers, results in the universal spam description consists of the most significant features. Flows described by selected features and collected on Broadband Remote Access Server were analysed by an ensemble of created classifiers. The ensemble detected major sources of spam among senders IP addresses.}, keywords = {Detection, Flow analysis, Intrusion, Network, Network data sets, Spam detection, Systems (NIDS)}, pubstate = {published}, tppubtype = {inproceedings} } Spam detection based on flow-level statistics is a new approach in anti-spam techniques. The approach reduces number of collected data but still can obtain relative good results in a spam detection task. The main problems in the approach are selection of flow-level features that describe spam and detection of discrimination rules. In this work, flow-level model of spam is presented. The model describes spam subclasses and brings information about major features of a spam detection task. The model is the base for decision trees that detect spam. The analysis of detectors, which was learned from data collected from different mail servers, results in the universal spam description consists of the most significant features. Flows described by selected features and collected on Broadband Remote Access Server were analysed by an ensemble of created classifiers. The ensemble detected major sources of spam among senders IP addresses. |
Luckner, Marcin; Szyszko, Krzysztof RBF ensemble based on reduction of DAG structure Inproceedings Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 99–105, IEEE, Kraków, 2013. Abstract | Links | BibTeX | Tags: Accuracy, binary classifiers, Chebyshev approximation, classes similarity, Classification, classification cost reduction, DAG structure reduction, Directed Acyclic Graph, directed graphs, Euclidean distance, Glass, Kernel, learning (artificial intelligence), pattern classification, Radial Basis Function, radial basis function ensemble, radial basis function networks, RBF ensemble, recognition accuracy, Support Vector Machines, UCI repository @inproceedings{Luckner2013a, title = {RBF ensemble based on reduction of DAG structure}, author = {Marcin Luckner and Krzysztof Szyszko}, url = {https://fedcsis.org/proceedings/2013/pliks/334.pdf}, year = {2013}, date = {2013-01-01}, booktitle = {Proceedings of the 2013 Federated Conference on Computer Science and Information Systems}, pages = {99--105}, publisher = {IEEE}, address = {Kraków}, abstract = {Binary classifiers are grouped into an ensemble to solve multi-class problems. One of proposed ensemble structure is a directed acyclic graph. In this structure, a classifier is created for each pair of classes. The number of classifiers can be reduced if groups of classes will be separated instead of individual classes. The proposed method is based on the similarity of classes defined as a distance between classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. In this paper, the proposed method is tested in variants based on various metrics. For the tests, several datasets from UCI repository was used and the results were compared with published works. The tests proved that grouping of radial basis functions into such ensemble reduces the classification cost and the recognition accuracy is not reduced significantly.}, keywords = {Accuracy, binary classifiers, Chebyshev approximation, classes similarity, Classification, classification cost reduction, DAG structure reduction, Directed Acyclic Graph, directed graphs, Euclidean distance, Glass, Kernel, learning (artificial intelligence), pattern classification, Radial Basis Function, radial basis function ensemble, radial basis function networks, RBF ensemble, recognition accuracy, Support Vector Machines, UCI repository}, pubstate = {published}, tppubtype = {inproceedings} } Binary classifiers are grouped into an ensemble to solve multi-class problems. One of proposed ensemble structure is a directed acyclic graph. In this structure, a classifier is created for each pair of classes. The number of classifiers can be reduced if groups of classes will be separated instead of individual classes. The proposed method is based on the similarity of classes defined as a distance between classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. In this paper, the proposed method is tested in variants based on various metrics. For the tests, several datasets from UCI repository was used and the results were compared with published works. The tests proved that grouping of radial basis functions into such ensemble reduces the classification cost and the recognition accuracy is not reduced significantly. |
Homenda, Wladyslaw; Luckner, Marcin; Pedrycz, Witold Classification with rejection : concepts and formal evaluations Inproceedings 8th International Conference on Knowledge, Information and Creativity Support, pp. 161–172, Kraków, 2013, ISBN: 9781479914845. BibTeX | Tags: Binary classifiers ensemble, Reclassification, Rejection rule @inproceedings{Homenda2013, title = {Classification with rejection : concepts and formal evaluations}, author = {Wladyslaw Homenda and Marcin Luckner and Witold Pedrycz}, isbn = {9781479914845}, year = {2013}, date = {2013-01-01}, booktitle = {8th International Conference on Knowledge, Information and Creativity Support}, pages = {161--172}, address = {Kraków}, keywords = {Binary classifiers ensemble, Reclassification, Rejection rule}, pubstate = {published}, tppubtype = {inproceedings} } |
Furtak, J; Grzenda, Maciej; ł, Jaros; Luckner, Marcin; Szmit, M; Afonso, J; Baghdadi, Y; Benslimane, S M; Chainbi, W; Chojnacki, A; Dabrowski, A; Davies, J; Fern?ndez, A; Frankowski, J; Fuchs, L; Furtak, J; Gaaloul, W; Garc?a-Dom?nguez, A; Garc?a-Osorio, C; Grabowski, S; Kiedrowicz, M; Korbel, P; Kowalczyk, E; Kowalski, A; Macukow, B Frontiers in network applications, network systems and web services Inproceedings 2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013, 2013, ISBN: 9781467344715. BibTeX | Tags: @inproceedings{Furtak2013, title = {Frontiers in network applications, network systems and web services}, author = {J Furtak and Maciej Grzenda and Jaros{ł}aw Legierski and Marcin Luckner and M Szmit and J Afonso and Y Baghdadi and S M Benslimane and W Chainbi and A Chojnacki and A Dabrowski and J Davies and A Fern?ndez and J Frankowski and L Fuchs and J Furtak and W Gaaloul and A Garc?a-Dom?nguez and C Garc?a-Osorio and S Grabowski and M Kiedrowicz and P Korbel and E Kowalczyk and A Kowalski and B Macukow}, isbn = {9781467344715}, year = {2013}, date = {2013-01-01}, booktitle = {2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Rudzinski, Jacek; Luckner, Marcin Low-cost computer vision based automatic scoring of shooting targets Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 185–195, 2013, ISSN: 03029743. Abstract | Links | BibTeX | Tags: Computer vision, Hough transform, Pattern recognition, Score estimation @inproceedings{Rudzinski2013, title = {Low-cost computer vision based automatic scoring of shooting targets}, author = {Jacek Rudzinski and Marcin Luckner}, doi = {10.1007/978-3-642-37343-5_19}, issn = {03029743}, year = {2013}, date = {2013-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {7828 LNAI}, pages = {185--195}, abstract = {This paper introduces an automatic scoring algorithm on shooting target based on computer vision techniques. As opposed to professional solutions, proposed system requires no additional equipment and relies solely on existing straightforward image processing such as the Prewitt edge detection and the Hough transformation. Experimental results show that the method can obtain high quality scoring. The proposed algorithm detects holes with 99 percent, resulting in 92 percent after eliminating false positives. The average error on the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lower than a tournament-scoring threshold. Therefore the system can be suitable for amateur shooters interested in professional (tournament-grade) accuracy. ? 2013 Springer-Verlag.}, keywords = {Computer vision, Hough transform, Pattern recognition, Score estimation}, pubstate = {published}, tppubtype = {inproceedings} } This paper introduces an automatic scoring algorithm on shooting target based on computer vision techniques. As opposed to professional solutions, proposed system requires no additional equipment and relies solely on existing straightforward image processing such as the Prewitt edge detection and the Hough transformation. Experimental results show that the method can obtain high quality scoring. The proposed algorithm detects holes with 99 percent, resulting in 92 percent after eliminating false positives. The average error on the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lower than a tournament-scoring threshold. Therefore the system can be suitable for amateur shooters interested in professional (tournament-grade) accuracy. ? 2013 Springer-Verlag. |
Sroka, Adrian; Luckner, Marcin Tree Symbols Detection for Green Space Estimation Incollection Advanced Concepts for Intelligent Vision Systems, Acivs 2013, pp. 526–537, 2013. @incollection{Sroka2013, title = {Tree Symbols Detection for Green Space Estimation}, author = {Adrian Sroka and Marcin Luckner}, url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000332973500047&KeyUID=WOS:000332973500047}, year = {2013}, date = {2013-01-01}, booktitle = {Advanced Concepts for Intelligent Vision Systems, Acivs 2013}, pages = {526--537}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } |
Luckner, Marcin; Filasiak, Robert Reference data sets for spam detection: Creation, analysis, propagation Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 212–221, 2013, ISSN: 03029743. Abstract | Links | BibTeX | Tags: Anomaly detection, Flow analysis, Hybrid classifiers, Reference sets, Spam detection @inproceedings{Luckner2013, title = {Reference data sets for spam detection: Creation, analysis, propagation}, author = {Marcin Luckner and Robert Filasiak}, doi = {10.1007/978-3-642-40846-5_22}, issn = {03029743}, year = {2013}, date = {2013-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8073 LNAI}, pages = {212--221}, abstract = {A reference set is a set of data of network traffic whose form and content allows detecting an event or a group of events. Realistic and representative datasets based on real traffic can improve research in the fields of intruders and anomaly detection. Creating reference sets tackles a number of issues such as the collection and storage of large volumes of data, the privacy of information and the relevance of collected events. Moreover, rare events are hard to analyse among background traffic and need specialist detection tools. One of the common problems that can be detected in network traffic is spam. This paper presents the methodology for creating a network traffic reference set for spam detection. The methodology concerns the selection of significant features, the collection and storage of data, the analysis of the collected data, the enrichment of the data with additional events and the propagation of the set. Moreover, a hybrid classifier that detects spam on relatively high level is presented. textcopyright 2013 Springer-Verlag.}, keywords = {Anomaly detection, Flow analysis, Hybrid classifiers, Reference sets, Spam detection}, pubstate = {published}, tppubtype = {inproceedings} } A reference set is a set of data of network traffic whose form and content allows detecting an event or a group of events. Realistic and representative datasets based on real traffic can improve research in the fields of intruders and anomaly detection. Creating reference sets tackles a number of issues such as the collection and storage of large volumes of data, the privacy of information and the relevance of collected events. Moreover, rare events are hard to analyse among background traffic and need specialist detection tools. One of the common problems that can be detected in network traffic is spam. This paper presents the methodology for creating a network traffic reference set for spam detection. The methodology concerns the selection of significant features, the collection and storage of data, the analysis of the collected data, the enrichment of the data with additional events and the propagation of the set. Moreover, a hybrid classifier that detects spam on relatively high level is presented. textcopyright 2013 Springer-Verlag. |
2012 |
Luckner, Marcin; Izdebski, Waldemar Publication of Geodetic Documentation Center Resources on Internet Inproceedings Advanced Information Systems Engineering Lecture Notes in Computer Science Volume 7328, pp. 533–548, Springer Berlin Heidelberg, 2012. Abstract | Links | BibTeX | Tags: e-government, spatial data, vice, web feature service, web map service, web mapping @inproceedings{Luckner2012d, title = {Publication of Geodetic Documentation Center Resources on Internet}, author = {Marcin Luckner and Waldemar Izdebski}, url = {http://link.springer.com/chapter/10.1007/978-3-642-31095-9_35}, doi = {10.1007/978-3-642-31095-9_35}, year = {2012}, date = {2012-01-01}, booktitle = {Advanced Information Systems Engineering Lecture Notes in Computer Science Volume 7328}, pages = {533--548}, publisher = {Springer Berlin Heidelberg}, abstract = {Geodetic Documentation Centers collect geodetic and cartographic resources. The resources include spatial data and their metadata. European Union INSPIRE directive imposes an obligation on GDC to publish selected data in the Internet. In this paper, an adequate form of publication is discussed on the base of iGeoMap application. The Internet application iGeoMap merges data from various resources. Depending on the data to present, different types of resources are used. The application can publish spatial data from files (text or binary), a database specialized in spatial data service (PostgreSQL, ORACLE), or web services (Web Map Service, Web Feature Service). Utilization of various data sources by the application is presented in this paper. As a part of the subject, searches of the most popular data (parcels, address points, and control points) are discussed. Various data sources and searching mechanisms involved by the searches in iGeoMap are presented in use cases.}, keywords = {e-government, spatial data, vice, web feature service, web map service, web mapping}, pubstate = {published}, tppubtype = {inproceedings} } Geodetic Documentation Centers collect geodetic and cartographic resources. The resources include spatial data and their metadata. European Union INSPIRE directive imposes an obligation on GDC to publish selected data in the Internet. In this paper, an adequate form of publication is discussed on the base of iGeoMap application. The Internet application iGeoMap merges data from various resources. Depending on the data to present, different types of resources are used. The application can publish spatial data from files (text or binary), a database specialized in spatial data service (PostgreSQL, ORACLE), or web services (Web Map Service, Web Feature Service). Utilization of various data sources by the application is presented in this paper. As a part of the subject, searches of the most popular data (parcels, address points, and control points) are discussed. Various data sources and searching mechanisms involved by the searches in iGeoMap are presented in use cases. |
Luckner, Marcin Problem eliminacji nieprzystajcacych elementów w zadaniu rozpoznania wzorca Incollection Zastosowania metod statystycznych w badaniach naukowych IV, pp. 283–294, StatSoft, 2012. BibTeX | Tags: @incollection{Luckner2012ab, title = {Problem eliminacji nieprzystajcacych elementów w zadaniu rozpoznania wzorca}, author = {Marcin Luckner}, year = {2012}, date = {2012-01-01}, booktitle = {Zastosowania metod statystycznych w badaniach naukowych IV}, pages = {283--294}, publisher = {StatSoft}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } |
Bagrowski, Grzegorz; Luckner, Marcin Comparison of Corner Detectors for Revolving Inproceedings Artificial Intelligence and Soft Computing Lecture Notes in Computer Science, pp. 459–467, Springer Berlin Heidelberg, 2012. Abstract | Links | BibTeX | Tags: 3d modeling, Computer vision, corner detectors @inproceedings{Bagrowski2012b, title = {Comparison of Corner Detectors for Revolving}, author = {Grzegorz Bagrowski and Marcin Luckner}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-29347-4_53}, doi = {10.1007/978-3-642-29347-4_53}, year = {2012}, date = {2012-01-01}, booktitle = {Artificial Intelligence and Soft Computing Lecture Notes in Computer Science}, pages = {459--467}, publisher = {Springer Berlin Heidelberg}, abstract = {The paper contains test of corner detectors applied in finding characteristic points on 3D revolving objects. Five different algorithm are presented starting from historical Moravec detector and ending at newest ones, such as SUSAN and Trajkovic. Since the algorithms are compared from the perspective of use for 3D modeling, the count of detected points and their localization is compared. The modeling process uses a series of photos and requires finding a projection of 3D point to two or three subsequent photos. The quality of algorithms is discussed on the base of the ability to detect modeled objects' corners and immunity to noise. The last researched aspect is the computation cost. The presented tests show that the best results are given by Shi–Tomasi operator. The detector does find false corners on noisy images, thus SUSAN operator may be used instead.}, keywords = {3d modeling, Computer vision, corner detectors}, pubstate = {published}, tppubtype = {inproceedings} } The paper contains test of corner detectors applied in finding characteristic points on 3D revolving objects. Five different algorithm are presented starting from historical Moravec detector and ending at newest ones, such as SUSAN and Trajkovic. Since the algorithms are compared from the perspective of use for 3D modeling, the count of detected points and their localization is compared. The modeling process uses a series of photos and requires finding a projection of 3D point to two or three subsequent photos. The quality of algorithms is discussed on the base of the ability to detect modeled objects' corners and immunity to noise. The last researched aspect is the computation cost. The presented tests show that the best results are given by Shi–Tomasi operator. The detector does find false corners on noisy images, thus SUSAN operator may be used instead. |
Rudzinski, Jacek; Luckner, Marcin Automatic scoring of shooting targets with tournament precision Inproceedings Frontiers in Artificial Intelligence and Applications, pp. 324–334, 2012, ISSN: 09226389. Abstract | Links | BibTeX | Tags: Computer vision, Hough transform, Pattern recognition, Score estimation @inproceedings{Rudzinski2012b, title = {Automatic scoring of shooting targets with tournament precision}, author = {Jacek Rudzinski and Marcin Luckner}, doi = {10.3233/978-1-61499-105-2-324}, issn = {09226389}, year = {2012}, date = {2012-01-01}, booktitle = {Frontiers in Artificial Intelligence and Applications}, volume = {243}, pages = {324--334}, abstract = {This paper describes a computer vision based automatic scoring system of shooting targets. The system estimates scoring with a professional tournament precision, but is dedicated to amateur shooters and can work with photos taken by amateur cameras and mobile devices. The automatic scoring issue is divided into three problems: a target detection, a holes detection, and a hole analysis. The target is detected on the base of a bull-eye localization. The holes detection bases on the Hough transformation. The holes analysis localizes a position of hole's center. The position relative to detected scoring sections is a base for scoring. The proposed algorithm detects holes with 99 percent accuracy. An elimination of false positives results reduces the level of accepted holes to 92 percents. The average error for the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lesser than a tournament-scoring threshold. textcopyright 2012 The authors and IOS Press. All rights reserved.}, keywords = {Computer vision, Hough transform, Pattern recognition, Score estimation}, pubstate = {published}, tppubtype = {inproceedings} } This paper describes a computer vision based automatic scoring system of shooting targets. The system estimates scoring with a professional tournament precision, but is dedicated to amateur shooters and can work with photos taken by amateur cameras and mobile devices. The automatic scoring issue is divided into three problems: a target detection, a holes detection, and a hole analysis. The target is detected on the base of a bull-eye localization. The holes detection bases on the Hough transformation. The holes analysis localizes a position of hole's center. The position relative to detected scoring sections is a base for scoring. The proposed algorithm detects holes with 99 percent accuracy. An elimination of false positives results reduces the level of accepted holes to 92 percents. The average error for the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lesser than a tournament-scoring threshold. textcopyright 2012 The authors and IOS Press. All rights reserved. |
Rudzinski, Jacek; Luckner, Marcin Automatic scoring of shooting targets with tournament precision Inproceedings Frontiers in Artificial Intelligence and Applications, pp. 324–334, 2012, ISSN: 09226389. Abstract | Links | BibTeX | Tags: Computer vision, Hough transform, Pattern recognition, Score estimation @inproceedings{Rudzinski2012, title = {Automatic scoring of shooting targets with tournament precision}, author = {Jacek Rudzinski and Marcin Luckner}, doi = {10.3233/978-1-61499-105-2-324}, issn = {09226389}, year = {2012}, date = {2012-01-01}, booktitle = {Frontiers in Artificial Intelligence and Applications}, volume = {243}, pages = {324--334}, abstract = {This paper describes a computer vision based automatic scoring system of shooting targets. The system estimates scoring with a professional tournament precision, but is dedicated to amateur shooters and can work with photos taken by amateur cameras and mobile devices. The automatic scoring issue is divided into three problems: a target detection, a holes detection, and a hole analysis. The target is detected on the base of a bull-eye localization. The holes detection bases on the Hough transformation. The holes analysis localizes a position of hole's center. The position relative to detected scoring sections is a base for scoring. The proposed algorithm detects holes with 99 percent accuracy. An elimination of false positives results reduces the level of accepted holes to 92 percents. The average error for the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lesser than a tournament-scoring threshold. textcopyright 2012 The authors and IOS Press. All rights reserved.}, keywords = {Computer vision, Hough transform, Pattern recognition, Score estimation}, pubstate = {published}, tppubtype = {inproceedings} } This paper describes a computer vision based automatic scoring system of shooting targets. The system estimates scoring with a professional tournament precision, but is dedicated to amateur shooters and can work with photos taken by amateur cameras and mobile devices. The automatic scoring issue is divided into three problems: a target detection, a holes detection, and a hole analysis. The target is detected on the base of a bull-eye localization. The holes detection bases on the Hough transformation. The holes analysis localizes a position of hole's center. The position relative to detected scoring sections is a base for scoring. The proposed algorithm detects holes with 99 percent accuracy. An elimination of false positives results reduces the level of accepted holes to 92 percents. The average error for the automatic score estimation is 0.05 points. The estimation error for over 91 percent holes is lesser than a tournament-scoring threshold. textcopyright 2012 The authors and IOS Press. All rights reserved. |
Luckner, Marcin; Izdebski, Waldemar Publication of Geodetic Documentation Center Resources on Internet Inproceedings Advanced Information Systems Engineering Lecture Notes in Computer Science Volume 7328, pp. 533–548, Springer Berlin Heidelberg, 2012. Abstract | Links | BibTeX | Tags: e-government, spatial data, vice, web feature service, web map service, web mapping @inproceedings{Luckner2012b, title = {Publication of Geodetic Documentation Center Resources on Internet}, author = {Marcin Luckner and Waldemar Izdebski}, url = {http://link.springer.com/chapter/10.1007/978-3-642-31095-9_35}, doi = {10.1007/978-3-642-31095-9_35}, year = {2012}, date = {2012-01-01}, booktitle = {Advanced Information Systems Engineering Lecture Notes in Computer Science Volume 7328}, pages = {533--548}, publisher = {Springer Berlin Heidelberg}, abstract = {Geodetic Documentation Centers collect geodetic and cartographic resources. The resources include spatial data and their metadata. European Union INSPIRE directive imposes an obligation on GDC to publish selected data in the Internet. In this paper, an adequate form of publication is discussed on the base of iGeoMap application. The Internet application iGeoMap merges data from various resources. Depending on the data to present, different types of resources are used. The application can publish spatial data from files (text or binary), a database specialized in spatial data service (PostgreSQL, ORACLE), or web services (Web Map Service, Web Feature Service). Utilization of various data sources by the application is presented in this paper. As a part of the subject, searches of the most popular data (parcels, address points, and control points) are discussed. Various data sources and searching mechanisms involved by the searches in iGeoMap are presented in use cases.}, keywords = {e-government, spatial data, vice, web feature service, web map service, web mapping}, pubstate = {published}, tppubtype = {inproceedings} } Geodetic Documentation Centers collect geodetic and cartographic resources. The resources include spatial data and their metadata. European Union INSPIRE directive imposes an obligation on GDC to publish selected data in the Internet. In this paper, an adequate form of publication is discussed on the base of iGeoMap application. The Internet application iGeoMap merges data from various resources. Depending on the data to present, different types of resources are used. The application can publish spatial data from files (text or binary), a database specialized in spatial data service (PostgreSQL, ORACLE), or web services (Web Map Service, Web Feature Service). Utilization of various data sources by the application is presented in this paper. As a part of the subject, searches of the most popular data (parcels, address points, and control points) are discussed. Various data sources and searching mechanisms involved by the searches in iGeoMap are presented in use cases. |
Bagrowski, Grzegorz; Luckner, Marcin Comparison of Corner Detectors for Revolving Inproceedings Artificial Intelligence and Soft Computing Lecture Notes in Computer Science, pp. 459–467, Springer Berlin Heidelberg, 2012. Abstract | Links | BibTeX | Tags: 3d modeling, Computer vision, corner detectors @inproceedings{Bagrowski2012, title = {Comparison of Corner Detectors for Revolving}, author = {Grzegorz Bagrowski and Marcin Luckner}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-29347-4_53}, doi = {10.1007/978-3-642-29347-4_53}, year = {2012}, date = {2012-01-01}, booktitle = {Artificial Intelligence and Soft Computing Lecture Notes in Computer Science}, pages = {459--467}, publisher = {Springer Berlin Heidelberg}, abstract = {The paper contains test of corner detectors applied in finding characteristic points on 3D revolving objects. Five different algorithm are presented starting from historical Moravec detector and ending at newest ones, such as SUSAN and Trajkovic. Since the algorithms are compared from the perspective of use for 3D modeling, the count of detected points and their localization is compared. The modeling process uses a series of photos and requires finding a projection of 3D point to two or three subsequent photos. The quality of algorithms is discussed on the base of the ability to detect modeled objects' corners and immunity to noise. The last researched aspect is the computation cost. The presented tests show that the best results are given by Shi–Tomasi operator. The detector does find false corners on noisy images, thus SUSAN operator may be used instead.}, keywords = {3d modeling, Computer vision, corner detectors}, pubstate = {published}, tppubtype = {inproceedings} } The paper contains test of corner detectors applied in finding characteristic points on 3D revolving objects. Five different algorithm are presented starting from historical Moravec detector and ending at newest ones, such as SUSAN and Trajkovic. Since the algorithms are compared from the perspective of use for 3D modeling, the count of detected points and their localization is compared. The modeling process uses a series of photos and requires finding a projection of 3D point to two or three subsequent photos. The quality of algorithms is discussed on the base of the ability to detect modeled objects' corners and immunity to noise. The last researched aspect is the computation cost. The presented tests show that the best results are given by Shi–Tomasi operator. The detector does find false corners on noisy images, thus SUSAN operator may be used instead. |
Luckner, Marcin Problem eliminacji nieprzystajcacych elementów w zadaniu rozpoznania wzorca Incollection Zastosowania metod statystycznych w badaniach naukowych IV, pp. 283–294, StatSoft, 2012. BibTeX | Tags: @incollection{Luckner2012a, title = {Problem eliminacji nieprzystajcacych elementów w zadaniu rozpoznania wzorca}, author = {Marcin Luckner}, year = {2012}, date = {2012-01-01}, booktitle = {Zastosowania metod statystycznych w badaniach naukowych IV}, pages = {283--294}, publisher = {StatSoft}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } |
2011 |
Luckner, Marcin Reducing Number of Classifiers in DAGSVM Based on Class Similarity Inproceedings Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science, pp. 514–523, Springer Berlin Heidelberg, 2011. Abstract | Links | BibTeX | Tags: Classification, Directed Acyclic Graph, One–Against–One, Support Vector Machines @inproceedings{Luckner2011ab, title = {Reducing Number of Classifiers in DAGSVM Based on Class Similarity}, author = {Marcin Luckner}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-24085-0_53}, doi = {10.1007/978-3-642-24085-0_53}, year = {2011}, date = {2011-01-01}, booktitle = {Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science}, pages = {514--523}, publisher = {Springer Berlin Heidelberg}, abstract = {Support Vector Machines are excellent binary classifiers. In case of multi–class classification problems individual classifiers can be collected into a directed acyclic graph structure DAGSVM. Such structure implements One-Against-One strategy. In this strategy a split is created for each pair of classes, but, because of hierarchical structure, only a part of them is used in the single classification process. The number of classifiers may be reduced if their classification tasks will be changed from separation of individual classes into separation of groups of classes. The proposed method is based on the similarity of classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. This solution reduces the classification cost. At the same time the recognition accuracy is not reduced in a significant way. Moreover, a number of SV, which influences on the learning time will not grow rapidly.}, keywords = {Classification, Directed Acyclic Graph, One–Against–One, Support Vector Machines}, pubstate = {published}, tppubtype = {inproceedings} } Support Vector Machines are excellent binary classifiers. In case of multi–class classification problems individual classifiers can be collected into a directed acyclic graph structure DAGSVM. Such structure implements One-Against-One strategy. In this strategy a split is created for each pair of classes, but, because of hierarchical structure, only a part of them is used in the single classification process. The number of classifiers may be reduced if their classification tasks will be changed from separation of individual classes into separation of groups of classes. The proposed method is based on the similarity of classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. This solution reduces the classification cost. At the same time the recognition accuracy is not reduced in a significant way. Moreover, a number of SV, which influences on the learning time will not grow rapidly. |
Luckner, Marcin Multiclass SVM classification using graphs calibrated by similarity between classes Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 435–444, 2011, ISSN: 03029743. Links | BibTeX | Tags: Classification, Decision trees, Directed Acyclic Graph, One-Against-All, One-Against-One @inproceedings{Luckner2011c, title = {Multiclass SVM classification using graphs calibrated by similarity between classes}, author = {Marcin Luckner}, doi = {10.1007/978-3-642-23866-6_46}, issn = {03029743}, year = {2011}, date = {2011-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {6884 LNAI}, number = {PART 4}, pages = {435--444}, keywords = {Classification, Decision trees, Directed Acyclic Graph, One-Against-All, One-Against-One}, pubstate = {published}, tppubtype = {inproceedings} } |
Grzenda, Maciej; Kaczmarski, Krzysztof; Kobos, Mateusz; Luckner, Marcin Geospatial presentation of purchase transactions data Inproceedings 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 291–296, 2011, ISBN: 978-83-60810-35-4. @inproceedings{Grzenda2011b, title = {Geospatial presentation of purchase transactions data}, author = {Maciej Grzenda and Krzysztof Kaczmarski and Mateusz Kobos and Marcin Luckner}, isbn = {978-83-60810-35-4}, year = {2011}, date = {2011-01-01}, booktitle = {2011 Federated Conference on Computer Science and Information Systems (FedCSIS)}, pages = {291--296}, abstract = {This paper presents a simple automatic system for small and middle Internet companies selling goods. The system combines temporal sales data with its geographical location and presents the resulting information on a map. Such an approach to data presentation should facilitate understanding of sales structure. This insight might be helpful in generating ideas on improving sales strategy; consequently improving revenues of the company. The system is flexible and generic — it can be adjusted to process and present the data within different levels of administrative division areas, using different hierarchies of sold goods. While describing the system, we also present its prototype that visualizes the data in an interactive way on a three-dimensional map.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents a simple automatic system for small and middle Internet companies selling goods. The system combines temporal sales data with its geographical location and presents the resulting information on a map. Such an approach to data presentation should facilitate understanding of sales structure. This insight might be helpful in generating ideas on improving sales strategy; consequently improving revenues of the company. The system is flexible and generic — it can be adjusted to process and present the data within different levels of administrative division areas, using different hierarchies of sold goods. While describing the system, we also present its prototype that visualizes the data in an interactive way on a three-dimensional map. |
Grzenda, Maciej; Kaczmarski, Krzysztof; Kobos, Mateusz; Luckner, Marcin Geospatial presentation of purchase transactions data Inproceedings 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 291–296, 2011, ISBN: 978-83-60810-35-4. @inproceedings{Grzenda2011, title = {Geospatial presentation of purchase transactions data}, author = {Maciej Grzenda and Krzysztof Kaczmarski and Mateusz Kobos and Marcin Luckner}, isbn = {978-83-60810-35-4}, year = {2011}, date = {2011-01-01}, booktitle = {2011 Federated Conference on Computer Science and Information Systems (FedCSIS)}, pages = {291--296}, abstract = {This paper presents a simple automatic system for small and middle Internet companies selling goods. The system combines temporal sales data with its geographical location and presents the resulting information on a map. Such an approach to data presentation should facilitate understanding of sales structure. This insight might be helpful in generating ideas on improving sales strategy; consequently improving revenues of the company. The system is flexible and generic — it can be adjusted to process and present the data within different levels of administrative division areas, using different hierarchies of sold goods. While describing the system, we also present its prototype that visualizes the data in an interactive way on a three-dimensional map.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents a simple automatic system for small and middle Internet companies selling goods. The system combines temporal sales data with its geographical location and presents the resulting information on a map. Such an approach to data presentation should facilitate understanding of sales structure. This insight might be helpful in generating ideas on improving sales strategy; consequently improving revenues of the company. The system is flexible and generic — it can be adjusted to process and present the data within different levels of administrative division areas, using different hierarchies of sold goods. While describing the system, we also present its prototype that visualizes the data in an interactive way on a three-dimensional map. |
Luckner, Marcin Reducing Number of Classifiers in DAGSVM Based on Class Similarity Inproceedings Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science, pp. 514–523, Springer Berlin Heidelberg, 2011. Abstract | Links | BibTeX | Tags: Classification, Directed Acyclic Graph, One–Against–One, Support Vector Machines @inproceedings{Luckner2011a, title = {Reducing Number of Classifiers in DAGSVM Based on Class Similarity}, author = {Marcin Luckner}, url = {http://link.springer.com/chapter/10.1007%2F978-3-642-24085-0_53}, doi = {10.1007/978-3-642-24085-0_53}, year = {2011}, date = {2011-01-01}, booktitle = {Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science}, pages = {514--523}, publisher = {Springer Berlin Heidelberg}, abstract = {Support Vector Machines are excellent binary classifiers. In case of multi–class classification problems individual classifiers can be collected into a directed acyclic graph structure DAGSVM. Such structure implements One-Against-One strategy. In this strategy a split is created for each pair of classes, but, because of hierarchical structure, only a part of them is used in the single classification process. The number of classifiers may be reduced if their classification tasks will be changed from separation of individual classes into separation of groups of classes. The proposed method is based on the similarity of classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. This solution reduces the classification cost. At the same time the recognition accuracy is not reduced in a significant way. Moreover, a number of SV, which influences on the learning time will not grow rapidly.}, keywords = {Classification, Directed Acyclic Graph, One–Against–One, Support Vector Machines}, pubstate = {published}, tppubtype = {inproceedings} } Support Vector Machines are excellent binary classifiers. In case of multi–class classification problems individual classifiers can be collected into a directed acyclic graph structure DAGSVM. Such structure implements One-Against-One strategy. In this strategy a split is created for each pair of classes, but, because of hierarchical structure, only a part of them is used in the single classification process. The number of classifiers may be reduced if their classification tasks will be changed from separation of individual classes into separation of groups of classes. The proposed method is based on the similarity of classes. For near classes the structure of DAG stays immutable. For the distant classes more than one is separated with a single classifier. This solution reduces the classification cost. At the same time the recognition accuracy is not reduced in a significant way. Moreover, a number of SV, which influences on the learning time will not grow rapidly. |
Luckner, Marcin Multiclass SVM classification using graphs calibrated by similarity between classes Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 435–444, 2011, ISSN: 03029743. Links | BibTeX | Tags: Classification, Decision trees, Directed Acyclic Graph, One-Against-All, One-Against-One @inproceedings{Luckner2011, title = {Multiclass SVM classification using graphs calibrated by similarity between classes}, author = {Marcin Luckner}, doi = {10.1007/978-3-642-23866-6_46}, issn = {03029743}, year = {2011}, date = {2011-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {6884 LNAI}, number = {PART 4}, pages = {435--444}, keywords = {Classification, Decision trees, Directed Acyclic Graph, One-Against-All, One-Against-One}, pubstate = {published}, tppubtype = {inproceedings} } |
2008 |
Luckner, Marcin Distances Tree as SVM Ensemble in Digits Recognition Task Inproceedings Proceedings of the 11th Joint Conference on Information Sciences, 2008. @inproceedings{Luckner2008d, title = {Distances Tree as SVM Ensemble in Digits Recognition Task}, author = {Marcin Luckner}, url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000269122600074&KeyUID=WOS:000269122600074}, year = {2008}, date = {2008-01-01}, booktitle = {Proceedings of the 11th Joint Conference on Information Sciences}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Luckner, Marcin Comparison of hierarchical svm structures in letters recognition task Inproceedings IEEE CIS-Poland Chapter Edited Volume, Warsaw, Poland, 2008. Links | BibTeX | Tags: decisions tree, hierar-, Pattern recognition, Support Vector Machines @inproceedings{Luckner2008ab, title = {Comparison of hierarchical svm structures in letters recognition task}, author = {Marcin Luckner}, url = {http://www.academia.edu/8882762/Comparison_of_hierarchical_svm_structures_in_letters_recognition_task}, year = {2008}, date = {2008-01-01}, booktitle = {IEEE CIS-Poland Chapter Edited Volume}, address = {Warsaw, Poland}, keywords = {decisions tree, hierar-, Pattern recognition, Support Vector Machines}, pubstate = {published}, tppubtype = {inproceedings} } |
Luckner, Marcin Comparison of hierarchical svm structures in letters recognition task Inproceedings IEEE CIS-Poland Chapter Edited Volume, Warsaw, Poland, 2008. Links | BibTeX | Tags: decisions tree, hierar-, Pattern recognition, Support Vector Machines @inproceedings{Luckner2008a, title = {Comparison of hierarchical svm structures in letters recognition task}, author = {Marcin Luckner}, url = {http://www.academia.edu/8882762/Comparison_of_hierarchical_svm_structures_in_letters_recognition_task}, year = {2008}, date = {2008-01-01}, booktitle = {IEEE CIS-Poland Chapter Edited Volume}, address = {Warsaw, Poland}, keywords = {decisions tree, hierar-, Pattern recognition, Support Vector Machines}, pubstate = {published}, tppubtype = {inproceedings} } |
Luckner, Marcin Distances Tree as SVM Ensemble in Digits Recognition Task Inproceedings Proceedings of the 11th Joint Conference on Information Sciences, 2008. @inproceedings{Luckner2008, title = {Distances Tree as SVM Ensemble in Digits Recognition Task}, author = {Marcin Luckner}, url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000269122600074&KeyUID=WOS:000269122600074}, year = {2008}, date = {2008-01-01}, booktitle = {Proceedings of the 11th Joint Conference on Information Sciences}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2007 |
Luckner, Marcin Recognition of Noised Patterns Using Non–Disruptive Learning Set Journal Article Journal of Digital Information Management, 5 (3), 2007. BibTeX | Tags: @article{Luckner2007b, title = {Recognition of Noised Patterns Using Non–Disruptive Learning Set}, author = {Marcin Luckner}, year = {2007}, date = {2007-01-01}, journal = {Journal of Digital Information Management}, volume = {5}, number = {3}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Luckner, Marcin Recognition of Noised Patterns Using Non–Disruptive Learning Set Journal Article Journal of Digital Information Management, 5 (3), 2007. BibTeX | Tags: @article{Luckner2007, title = {Recognition of Noised Patterns Using Non–Disruptive Learning Set}, author = {Marcin Luckner}, year = {2007}, date = {2007-01-01}, journal = {Journal of Digital Information Management}, volume = {5}, number = {3}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2006 |
Luckner, Marcin Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. Abstract | Links | BibTeX | Tags: Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition @inproceedings{Luckner2006d, title = {Recognition of Noised Patterns Using Non-Disruption Learning Set}, author = {Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4021499}, doi = {10.1109/ISDA.2006.223}, isbn = {0-7695-2528-8}, year = {2006}, date = {2006-10-01}, booktitle = {Sixth International Conference on Intelligent Systems Design and Applications}, volume = {1}, pages = {557--562}, publisher = {IEEE}, abstract = {In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones}, keywords = {Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition}, pubstate = {published}, tppubtype = {inproceedings} } In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones |
Luckner, Marcin; Homenda, Wladyslaw Braille Score Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 775–780, IEEE, 2006, ISBN: 0-7695-2528-8. Abstract | Links | BibTeX | Tags: artificial intelligence, blind people, Braille score, computer program, Engines, Geodesy, Geophysics computing, handicapped aids, Information science, Instruments, Mathematics, MIDI file, music, music notation, music processing, Optical character recognition software, Optical computing, Ordinary magnetoresistance, Pattern recognition, scores recognition @inproceedings{Luckner2006ab, title = {Braille Score}, author = {Marcin Luckner and Wladyslaw Homenda}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4021537}, doi = {10.1109/ISDA.2006.118}, isbn = {0-7695-2528-8}, year = {2006}, date = {2006-10-01}, booktitle = {Sixth International Conference on Intelligent Systems Design and Applications}, volume = {1}, pages = {775--780}, publisher = {IEEE}, abstract = {The paper presents a developing computer program that helps the blind people dealing with music notation. The program enables the full path of music processing: starting with a printed musical score and ending with MIDI file which can be performed by an electronic instrument. The recognition module based on an advanced artificial intelligence technology is an engine of the system. Recognized scores are converted into a special internal representation that allows conveying all niceties of music. A record can be also processed with an editor module that is particularly projected for the blind people}, keywords = {artificial intelligence, blind people, Braille score, computer program, Engines, Geodesy, Geophysics computing, handicapped aids, Information science, Instruments, Mathematics, MIDI file, music, music notation, music processing, Optical character recognition software, Optical computing, Ordinary magnetoresistance, Pattern recognition, scores recognition}, pubstate = {published}, tppubtype = {inproceedings} } The paper presents a developing computer program that helps the blind people dealing with music notation. The program enables the full path of music processing: starting with a printed musical score and ending with MIDI file which can be performed by an electronic instrument. The recognition module based on an advanced artificial intelligence technology is an engine of the system. Recognized scores are converted into a special internal representation that allows conveying all niceties of music. A record can be also processed with an editor module that is particularly projected for the blind people |
Luckner, Marcin; Homenda, Wladyslaw Braille Score Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 775–780, IEEE, 2006, ISBN: 0-7695-2528-8. Abstract | Links | BibTeX | Tags: artificial intelligence, blind people, Braille score, computer program, Engines, Geodesy, Geophysics computing, handicapped aids, Information science, Instruments, Mathematics, MIDI file, music, music notation, music processing, Optical character recognition software, Optical computing, Ordinary magnetoresistance, Pattern recognition, scores recognition @inproceedings{Luckner2006a, title = {Braille Score}, author = {Marcin Luckner and Wladyslaw Homenda}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4021537}, doi = {10.1109/ISDA.2006.118}, isbn = {0-7695-2528-8}, year = {2006}, date = {2006-10-01}, booktitle = {Sixth International Conference on Intelligent Systems Design and Applications}, volume = {1}, pages = {775--780}, publisher = {IEEE}, abstract = {The paper presents a developing computer program that helps the blind people dealing with music notation. The program enables the full path of music processing: starting with a printed musical score and ending with MIDI file which can be performed by an electronic instrument. The recognition module based on an advanced artificial intelligence technology is an engine of the system. Recognized scores are converted into a special internal representation that allows conveying all niceties of music. A record can be also processed with an editor module that is particularly projected for the blind people}, keywords = {artificial intelligence, blind people, Braille score, computer program, Engines, Geodesy, Geophysics computing, handicapped aids, Information science, Instruments, Mathematics, MIDI file, music, music notation, music processing, Optical character recognition software, Optical computing, Ordinary magnetoresistance, Pattern recognition, scores recognition}, pubstate = {published}, tppubtype = {inproceedings} } The paper presents a developing computer program that helps the blind people dealing with music notation. The program enables the full path of music processing: starting with a printed musical score and ending with MIDI file which can be performed by an electronic instrument. The recognition module based on an advanced artificial intelligence technology is an engine of the system. Recognized scores are converted into a special internal representation that allows conveying all niceties of music. A record can be also processed with an editor module that is particularly projected for the blind people |
Luckner, Marcin Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. Abstract | Links | BibTeX | Tags: Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition @inproceedings{Luckner2006, title = {Recognition of Noised Patterns Using Non-Disruption Learning Set}, author = {Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4021499}, doi = {10.1109/ISDA.2006.223}, isbn = {0-7695-2528-8}, year = {2006}, date = {2006-10-01}, booktitle = {Sixth International Conference on Intelligent Systems Design and Applications}, volume = {1}, pages = {557--562}, publisher = {IEEE}, abstract = {In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones}, keywords = {Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition}, pubstate = {published}, tppubtype = {inproceedings} } In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones |
ł, W{ł}adys; Luckner, Marcin Automatic Knowledge Acquisition: Recognizing Music Notation with Methods of Centroids and Classifications Trees Inproceedings The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 3382–3388, IEEE, 2006, ISSN: 10987576. Abstract | Links | BibTeX | Tags: centroids, Classification tree analysis, classifications trees, classifiers, Decision trees, feature extraction, Knowledge acquisition, Multiple signal classification, music, music notation recognition, music symbols recognition, Neural networks, Optical character recognition software, Ordinary magnetoresistance, pattern classification, Pattern recognition, Printing, Text recognition, Tiles @inproceedings{Homenda2006b, title = {Automatic Knowledge Acquisition: Recognizing Music Notation with Methods of Centroids and Classifications Trees}, author = {W{ł}adys{ł}aw Homenda and Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1716561}, doi = {10.1109/IJCNN.2006.247339}, issn = {10987576}, year = {2006}, date = {2006-01-01}, booktitle = {The 2006 IEEE International Joint Conference on Neural Network Proceedings}, pages = {3382--3388}, publisher = {IEEE}, abstract = {This paper presents a pattern recognition study aimed al music symbols recognition. The study is focused on classification methods of music symbols based on decision trees and clustering method applied to classes of music symbols that face classification problems. Classification is made on the basis of extracted features. A comparison of selected classifiers was made on some classes of nutation symbols distorted by a variety of factors as image noise, printing defects, different fonts, skew and curvature of scanning, overlapped symbols.}, keywords = {centroids, Classification tree analysis, classifications trees, classifiers, Decision trees, feature extraction, Knowledge acquisition, Multiple signal classification, music, music notation recognition, music symbols recognition, Neural networks, Optical character recognition software, Ordinary magnetoresistance, pattern classification, Pattern recognition, Printing, Text recognition, Tiles}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents a pattern recognition study aimed al music symbols recognition. The study is focused on classification methods of music symbols based on decision trees and clustering method applied to classes of music symbols that face classification problems. Classification is made on the basis of extracted features. A comparison of selected classifiers was made on some classes of nutation symbols distorted by a variety of factors as image noise, printing defects, different fonts, skew and curvature of scanning, overlapped symbols. |
ł, W{ł}adys; Luckner, Marcin Automatic Knowledge Acquisition: Recognizing Music Notation with Methods of Centroids and Classifications Trees Inproceedings The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 3382–3388, IEEE, 2006, ISSN: 10987576. Abstract | Links | BibTeX | Tags: centroids, Classification tree analysis, classifications trees, classifiers, Decision trees, feature extraction, Knowledge acquisition, Multiple signal classification, music, music notation recognition, music symbols recognition, Neural networks, Optical character recognition software, Ordinary magnetoresistance, pattern classification, Pattern recognition, Printing, Text recognition, Tiles @inproceedings{Homenda2006, title = {Automatic Knowledge Acquisition: Recognizing Music Notation with Methods of Centroids and Classifications Trees}, author = {W{ł}adys{ł}aw Homenda and Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1716561}, doi = {10.1109/IJCNN.2006.247339}, issn = {10987576}, year = {2006}, date = {2006-01-01}, booktitle = {The 2006 IEEE International Joint Conference on Neural Network Proceedings}, pages = {3382--3388}, publisher = {IEEE}, abstract = {This paper presents a pattern recognition study aimed al music symbols recognition. The study is focused on classification methods of music symbols based on decision trees and clustering method applied to classes of music symbols that face classification problems. Classification is made on the basis of extracted features. A comparison of selected classifiers was made on some classes of nutation symbols distorted by a variety of factors as image noise, printing defects, different fonts, skew and curvature of scanning, overlapped symbols.}, keywords = {centroids, Classification tree analysis, classifications trees, classifiers, Decision trees, feature extraction, Knowledge acquisition, Multiple signal classification, music, music notation recognition, music symbols recognition, Neural networks, Optical character recognition software, Ordinary magnetoresistance, pattern classification, Pattern recognition, Printing, Text recognition, Tiles}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents a pattern recognition study aimed al music symbols recognition. The study is focused on classification methods of music symbols based on decision trees and clustering method applied to classes of music symbols that face classification problems. Classification is made on the basis of extracted features. A comparison of selected classifiers was made on some classes of nutation symbols distorted by a variety of factors as image noise, printing defects, different fonts, skew and curvature of scanning, overlapped symbols. |
2005 |
Homenda, Wladyslaw; Luckner, Marcin Hierarchical ocr system for texts in musical scores Inproceedings Eleventh International Fuzzy Systems Association World Congress,, Beijing, China, 2005. Abstract | BibTeX | Tags: linear networks, ocr, Pattern recognition @inproceedings{Homenda2005b, title = {Hierarchical ocr system for texts in musical scores}, author = {Wladyslaw Homenda and Marcin Luckner}, year = {2005}, date = {2005-01-01}, booktitle = {Eleventh International Fuzzy Systems Association World Congress,}, address = {Beijing, China}, abstract = {This paper presents a study on hierarchical OCR system specialized and applied in music text recognition. Recognition is performed by several modules in some stages creating a hierarchical structure. Main modules used in the system carry out filtration, classification, rejection and additional analysis . The classification module is based on linear splits of a data space. Additional modules are aimed in improving results and accelerating run time. Effectiveness of the proposed system exceeds 93 percent.}, keywords = {linear networks, ocr, Pattern recognition}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents a study on hierarchical OCR system specialized and applied in music text recognition. Recognition is performed by several modules in some stages creating a hierarchical structure. Main modules used in the system carry out filtration, classification, rejection and additional analysis . The classification module is based on linear splits of a data space. Additional modules are aimed in improving results and accelerating run time. Effectiveness of the proposed system exceeds 93 percent. |
Publications
2014 |
3D model reconstruction and evaluation using a collection of points extracted from the series of photographs Inproceedings 2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014, pp. 669–677, 2014, ISBN: 9788360810583. |
Stable web spam detection using features based on lexical items Journal Article Computers & Security, 46 , pp. 79–93, 2014, ISSN: 01674048. |
On the testing of network cyber threat detection methods on spam example Journal Article Annales des Telecommunications/Annals of Telecommunications, 69 (7-8), pp. 363–377, 2014, ISSN: 19589395. |
Global and local rejection option in multi-classification task Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 483–490, 2014, ISSN: 16113349. |
Classification with rejection based on various SVM techniques Inproceedings Proceedings of the International Joint Conference on Neural Networks, pp. 3480–3487, 2014, ISBN: 9781479914845. |
Classification with rejection based on various SVM techniques Inproceedings Proceedings of the International Joint Conference on Neural Networks, pp. 3480–3487, 2014, ISBN: 9781479914845. |
Global and local rejection option in multi-classification task Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 483–490, 2014, ISSN: 16113349. |
On the testing of network cyber threat detection methods on spam example Journal Article Annales des Telecommunications/Annals of Telecommunications, 69 (7-8), pp. 363–377, 2014, ISSN: 19589395. |
Stable web spam detection using features based on lexical items Journal Article Computers & Security, 46 , pp. 79–93, 2014, ISSN: 01674048. |
2013 |
Tree Symbols Detection for Green Space Estimation Incollection Advanced Concepts for Intelligent Vision Systems, Acivs 2013, pp. 526–537, 2013. |
Reference data sets for spam detection: Creation, analysis, propagation Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 212–221, 2013, ISSN: 03029743. |
RBF ensemble based on reduction of DAG structure Inproceedings Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 99–105, IEEE, Kraków, 2013. |
Low-cost computer vision based automatic scoring of shooting targets Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 185–195, 2013, ISSN: 03029743. |
Frontiers in network applications, network systems and web services Inproceedings 2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013, 2013, ISBN: 9781467344715. |
Flow-level Spam Modelling using separate data sources Inproceedings Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, pp. 91 – 98, IEEE, 2013. |
Classification with rejection : concepts and formal evaluations Inproceedings 8th International Conference on Knowledge, Information and Creativity Support, pp. 161–172, Kraków, 2013, ISBN: 9781479914845. |
Flow-level Spam Modelling using separate data sources Inproceedings Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on, pp. 91 – 98, IEEE, 2013. |
RBF ensemble based on reduction of DAG structure Inproceedings Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, pp. 99–105, IEEE, Kraków, 2013. |
Classification with rejection : concepts and formal evaluations Inproceedings 8th International Conference on Knowledge, Information and Creativity Support, pp. 161–172, Kraków, 2013, ISBN: 9781479914845. |
Frontiers in network applications, network systems and web services Inproceedings 2013 Federated Conference on Computer Science and Information Systems, FedCSIS 2013, 2013, ISBN: 9781467344715. |
Low-cost computer vision based automatic scoring of shooting targets Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 185–195, 2013, ISSN: 03029743. |
Tree Symbols Detection for Green Space Estimation Incollection Advanced Concepts for Intelligent Vision Systems, Acivs 2013, pp. 526–537, 2013. |
Reference data sets for spam detection: Creation, analysis, propagation Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 212–221, 2013, ISSN: 03029743. |
2012 |
Publication of Geodetic Documentation Center Resources on Internet Inproceedings Advanced Information Systems Engineering Lecture Notes in Computer Science Volume 7328, pp. 533–548, Springer Berlin Heidelberg, 2012. |
Problem eliminacji nieprzystajcacych elementów w zadaniu rozpoznania wzorca Incollection Zastosowania metod statystycznych w badaniach naukowych IV, pp. 283–294, StatSoft, 2012. |
Comparison of Corner Detectors for Revolving Inproceedings Artificial Intelligence and Soft Computing Lecture Notes in Computer Science, pp. 459–467, Springer Berlin Heidelberg, 2012. |
Automatic scoring of shooting targets with tournament precision Inproceedings Frontiers in Artificial Intelligence and Applications, pp. 324–334, 2012, ISSN: 09226389. |
Automatic scoring of shooting targets with tournament precision Inproceedings Frontiers in Artificial Intelligence and Applications, pp. 324–334, 2012, ISSN: 09226389. |
Publication of Geodetic Documentation Center Resources on Internet Inproceedings Advanced Information Systems Engineering Lecture Notes in Computer Science Volume 7328, pp. 533–548, Springer Berlin Heidelberg, 2012. |
Comparison of Corner Detectors for Revolving Inproceedings Artificial Intelligence and Soft Computing Lecture Notes in Computer Science, pp. 459–467, Springer Berlin Heidelberg, 2012. |
Problem eliminacji nieprzystajcacych elementów w zadaniu rozpoznania wzorca Incollection Zastosowania metod statystycznych w badaniach naukowych IV, pp. 283–294, StatSoft, 2012. |
2011 |
Reducing Number of Classifiers in DAGSVM Based on Class Similarity Inproceedings Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science, pp. 514–523, Springer Berlin Heidelberg, 2011. |
Multiclass SVM classification using graphs calibrated by similarity between classes Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 435–444, 2011, ISSN: 03029743. |
Geospatial presentation of purchase transactions data Inproceedings 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 291–296, 2011, ISBN: 978-83-60810-35-4. |
Geospatial presentation of purchase transactions data Inproceedings 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 291–296, 2011, ISBN: 978-83-60810-35-4. |
Reducing Number of Classifiers in DAGSVM Based on Class Similarity Inproceedings Image Analysis and Processing – ICIAP 2011 Lecture Notes in Computer Science, pp. 514–523, Springer Berlin Heidelberg, 2011. |
Multiclass SVM classification using graphs calibrated by similarity between classes Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 435–444, 2011, ISSN: 03029743. |
2008 |
Distances Tree as SVM Ensemble in Digits Recognition Task Inproceedings Proceedings of the 11th Joint Conference on Information Sciences, 2008. |
Comparison of hierarchical svm structures in letters recognition task Inproceedings IEEE CIS-Poland Chapter Edited Volume, Warsaw, Poland, 2008. |
Comparison of hierarchical svm structures in letters recognition task Inproceedings IEEE CIS-Poland Chapter Edited Volume, Warsaw, Poland, 2008. |
Distances Tree as SVM Ensemble in Digits Recognition Task Inproceedings Proceedings of the 11th Joint Conference on Information Sciences, 2008. |
2007 |
Recognition of Noised Patterns Using Non–Disruptive Learning Set Journal Article Journal of Digital Information Management, 5 (3), 2007. |
Recognition of Noised Patterns Using Non–Disruptive Learning Set Journal Article Journal of Digital Information Management, 5 (3), 2007. |
2006 |
Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. |
Braille Score Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 775–780, IEEE, 2006, ISBN: 0-7695-2528-8. |
Braille Score Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 775–780, IEEE, 2006, ISBN: 0-7695-2528-8. |
Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. |
Automatic Knowledge Acquisition: Recognizing Music Notation with Methods of Centroids and Classifications Trees Inproceedings The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 3382–3388, IEEE, 2006, ISSN: 10987576. |
Automatic Knowledge Acquisition: Recognizing Music Notation with Methods of Centroids and Classifications Trees Inproceedings The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 3382–3388, IEEE, 2006, ISSN: 10987576. |
2005 |
Hierarchical ocr system for texts in musical scores Inproceedings Eleventh International Fuzzy Systems Association World Congress,, Beijing, China, 2005. |