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@misc{1204273,
author = {Majtner, Tomáš and Stoklasa, Roman and Svoboda, David},
keywords = {image descriptor; Pattern recognition; RSurf; HEp-2 cells;},
language = {eng},
institution = {Masarykova univerzita},
organization = {Masarykova univerzita},
title = {RSurf Texture Descriptor},
url = {http://cbia.fi.muni.cz/projects/rsurf-texture-descriptor.html},
year = {2014}
}
TY -
ID - 1204273
AU - Majtner, Tomáš - Stoklasa, Roman - Svoboda, David
PY - 2014
TI - RSurf Texture Descriptor
KW - image descriptor
KW - Pattern recognition
KW - RSurf
KW - HEp-2 cells;
UR - http://cbia.fi.muni.cz/projects/rsurf-texture-descriptor.html
L2 - http://cbia.fi.muni.cz/projects/rsurf-texture-descriptor.html
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 kNN classifier based solely on the proposed descriptor achieve the accuracy as high as 91.1%.
ER -
MAJTNER, Tomáš, Roman STOKLASA and David SVOBODA. \textit{RSurf Texture Descriptor}. 2014.
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