2006 |
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 |
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. |