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@inproceedings{1173479, author = {Majtner, Tomáš and Stoklasa, Roman and Svoboda, David}, address = {Los Alamitos, California}, booktitle = {22nd International Conference on Pattern Recognition}, doi = {http://dx.doi.org/10.1109/ICPR.2014.215}, keywords = {texture descriptor;rsurf;hep-2}, howpublished = {paměťový nosič}, language = {eng}, location = {Los Alamitos, California}, isbn = {978-1-4799-5208-3}, pages = {1194-1199}, publisher = {IEEE Computer Society}, title = {RSurf - the Efficient Texture-Based Descriptor for Fluorescence Microscopy Images of HEp-2 Cells}, year = {2014} }
TY - JOUR ID - 1173479 AU - Majtner, Tomáš - Stoklasa, Roman - Svoboda, David PY - 2014 TI - RSurf - the Efficient Texture-Based Descriptor for Fluorescence Microscopy Images of HEp-2 Cells PB - IEEE Computer Society CY - Los Alamitos, California SN - 9781479952083 KW - texture descriptor;rsurf;hep-2 N2 - In biomedical image analysis, object description and classification tasks are very common. Our work relates to the problem of classification of Human Epithelial (HEp-2) cells. Since the crucial part of each classification process is the feature extraction and selection, much attention should be concentrated to the development of proper image descriptors. In this article, we introduce a new efficient texture-based image descriptor for HEp-2 images. We compare proposed descriptor with LBP, Haralick features (GLCM statistics) and Tamura features using the public MIVIA HEp-2 Images Dataset. Our descriptor outperforms all previously mentioned approaches and the classifier based solely on the proposed descriptor is able to achieve the accuracy as high as 87.8%. ER -
MAJTNER, Tomáš, Roman STOKLASA and David SVOBODA. RSurf - the Efficient Texture-Based Descriptor for Fluorescence Microscopy Images of HEp-2 Cells. In \textit{22nd International Conference on Pattern Recognition}. Los Alamitos, California: IEEE Computer Society, 2014, p.~1194-1199. ISBN~978-1-4799-5208-3. Available from: https://dx.doi.org/10.1109/ICPR.2014.215.
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