Publications

@inproceedings{Homenda2014a,
title = {Pattern recognition with rejection: Application to handwritten digits},
author = {Wladyslaw Homenda and Marcin Luckner},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7077288},
doi = {10.1109/WICT.2014.7077288},
isbn = {978-1-4799-8115-1},
year = {2014},
date = {2014-12-01},
booktitle = {2014 4th World Congress on Information and Communication Technologies (WICT 2014)},
pages = {326--331},
publisher = {IEEE},
abstract = {The paper considers rejecting option in pattern recognition problem. Studied are native and foreign elements in a multi-class pattern recognition. Native elements are those included in recognized classes, they are known at the stage of classifier design. Foreign elements do not belong to recognized classes. Usually foreign elements are not known when classifier is designed. If foreign elements are classified to recognized classes, recognition quality is deteriorated. So then, they are classified to native classes, if they are not rejected. In such the case, recognition quality is deteriorated. Therefore, they should be rejected by a classifier, i.e. not classified to any class. Several attempts to rejection of foreign elements are investigated in this study.},
keywords = {Accuracy, Classification with rejection, Handwriting recognition, native and foreign elements, pattern recognition with rejection, Standards, Support Vector Machines, Testing, Text recognition},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Homenda2014ab,
title = {Pattern recognition with rejection: Application to handwritten digits},
author = {Wladyslaw Homenda and Marcin Luckner},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7077288},
doi = {10.1109/WICT.2014.7077288},
isbn = {978-1-4799-8115-1},
year = {2014},
date = {2014-12-01},
booktitle = {2014 4th World Congress on Information and Communication Technologies (WICT 2014)},
pages = {326--331},
publisher = {IEEE},
abstract = {The paper considers rejecting option in pattern recognition problem. Studied are native and foreign elements in a multi-class pattern recognition. Native elements are those included in recognized classes, they are known at the stage of classifier design. Foreign elements do not belong to recognized classes. Usually foreign elements are not known when classifier is designed. If foreign elements are classified to recognized classes, recognition quality is deteriorated. So then, they are classified to native classes, if they are not rejected. In such the case, recognition quality is deteriorated. Therefore, they should be rejected by a classifier, i.e. not classified to any class. Several attempts to rejection of foreign elements are investigated in this study.},
keywords = {Accuracy, Classification with rejection, Handwriting recognition, native and foreign elements, pattern recognition with rejection, Standards, Support Vector Machines, Testing, Text recognition},
pubstate = {published},
tppubtype = {inproceedings}
}

@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}
}

@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}
}