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{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 | 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; 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 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 |
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. |
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. |
Publications
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. |