2016 |
Wilkowski, Artur; Luckner, Marcin Low-cost canoe counting system for application in a natural environment Inproceedings Advances in Intelligent Systems and Computing, pp. 705–715, 2016, ISSN: 21945357. Abstract | Links | BibTeX | Tagi: Classification with rejection, Computer vision, Pattern recognition @inproceedings{Wilkowski2016, title = {Low-cost canoe counting system for application in a natural environment}, author = {Artur Wilkowski and Marcin Luckner}, doi = {10.1007/978-3-319-29357-8_61}, issn = {21945357}, year = {2016}, date = {2016-01-01}, booktitle = {Advances in Intelligent Systems and Computing}, volume = {440}, pages = {705--715}, abstract = {? Springer International Publishing Switzerland 2016.This paper presents low-cost system for counting canoes and canoeists to control cannoning tourist routes. The created system was implemented on Raspberry Pi 2 and the total cost of the tracking device is less than 200$. The proposed algorithmuses background subtraction and Support Vector Machines to track vessels and recognize canoes among them. The obtained results are rewarding as for low-cost solution. Depending on considered group of objects the accuracy of the algorithm reaches 84, 89.5, and 96% for canoes, vessels, and all objects respectively.}, keywords = {Classification with rejection, Computer vision, Pattern recognition}, pubstate = {published}, tppubtype = {inproceedings} } ? Springer International Publishing Switzerland 2016.This paper presents low-cost system for counting canoes and canoeists to control cannoning tourist routes. The created system was implemented on Raspberry Pi 2 and the total cost of the tracking device is less than 200$. The proposed algorithmuses background subtraction and Support Vector Machines to track vessels and recognize canoes among them. The obtained results are rewarding as for low-cost solution. Depending on considered group of objects the accuracy of the algorithm reaches 84, 89.5, and 96% for canoes, vessels, and all objects respectively. |
Wilkowski, Artur; Luckner, Marcin Low-cost canoe counting system for application in a natural environment Inproceedings Advances in Intelligent Systems and Computing, pp. 705–715, 2016, ISSN: 21945357. Abstract | Links | BibTeX | Tagi: Classification with rejection, Computer vision, Pattern recognition @inproceedings{Wilkowski2016b, title = {Low-cost canoe counting system for application in a natural environment}, author = {Artur Wilkowski and Marcin Luckner}, doi = {10.1007/978-3-319-29357-8_61}, issn = {21945357}, year = {2016}, date = {2016-01-01}, booktitle = {Advances in Intelligent Systems and Computing}, volume = {440}, pages = {705--715}, abstract = {? Springer International Publishing Switzerland 2016.This paper presents low-cost system for counting canoes and canoeists to control cannoning tourist routes. The created system was implemented on Raspberry Pi 2 and the total cost of the tracking device is less than 200$. The proposed algorithmuses background subtraction and Support Vector Machines to track vessels and recognize canoes among them. The obtained results are rewarding as for low-cost solution. Depending on considered group of objects the accuracy of the algorithm reaches 84, 89.5, and 96% for canoes, vessels, and all objects respectively.}, keywords = {Classification with rejection, Computer vision, Pattern recognition}, pubstate = {published}, tppubtype = {inproceedings} } ? Springer International Publishing Switzerland 2016.This paper presents low-cost system for counting canoes and canoeists to control cannoning tourist routes. The created system was implemented on Raspberry Pi 2 and the total cost of the tracking device is less than 200$. The proposed algorithmuses background subtraction and Support Vector Machines to track vessels and recognize canoes among them. The obtained results are rewarding as for low-cost solution. Depending on considered group of objects the accuracy of the algorithm reaches 84, 89.5, and 96% for canoes, vessels, and all objects respectively. |
2014 |
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 | Tagi: 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. |
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 | Tagi: 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. |
2013 |
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 | Tagi: 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. |
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 | Tagi: 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. |
2012 |
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 | Tagi: 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. |
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 | Tagi: 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. |
2008 |
Luckner, Marcin Comparison of hierarchical svm structures in letters recognition task Inproceedings IEEE CIS-Poland Chapter Edited Volume, Warsaw, Poland, 2008. Links | BibTeX | Tagi: 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 Comparison of hierarchical svm structures in letters recognition task Inproceedings IEEE CIS-Poland Chapter Edited Volume, Warsaw, Poland, 2008. Links | BibTeX | Tagi: 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} } |
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 | Tagi: 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 |
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 | Tagi: 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; 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 | Tagi: 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 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 | Tagi: 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 |
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 | Tagi: 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. |
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 | Tagi: 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. |
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 | Tagi: linear networks, ocr, Pattern recognition @inproceedings{Homenda2005, 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. |
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 | Tagi: 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. |
Publikacje
2016 |
Low-cost canoe counting system for application in a natural environment Inproceedings Advances in Intelligent Systems and Computing, pp. 705–715, 2016, ISSN: 21945357. |
Low-cost canoe counting system for application in a natural environment Inproceedings Advances in Intelligent Systems and Computing, pp. 705–715, 2016, ISSN: 21945357. |
2014 |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
Hierarchical ocr system for texts in musical scores Inproceedings Eleventh International Fuzzy Systems Association World Congress,, Beijing, China, 2005. |