Locally Rotation, Contrast, and Scale Invariant Descriptors for Texture Analysis Export

@article{citeulike:2369400, abstract = {Textures within real images vary in brightness, contrast, scale and skew as imaging conditions change. To enable recognition of textures in real images, it is necessary to employ a similarity measure which is invariant to these properties. Furthermore, since textures often appear on undulating surfaces, such invariances must necessarily be local rather than global. Despite these requirements, it is only relatively recently that texture recognition algorithms with local scale and affine invariance properties have begun to be reported. Typically, they comprise detecting feature points followed by geometric normalization prior to description. We describe a method based on invariant combinations of linear filters. Unlike previous methods, we introduce a novel family of filters, which provide scale invariance, resulting in a texture description invariant to local changes in orientation, contrast and scale and robust to local skew. Significantly, the family of filters enable local scale invariants to be defined without using a scale selection principle or a large number of filters. A texture discrimination method based on the \&\#x0C2;2 similarity measure applied to histograms derived from our filter responses outperforms existing methods for retrieval and classification results for both the Brodatz textures and the UIUC database, which has been designed to require local invariance.}, author = {Mellor, Matthew and Hong, Byung-Woo and Brady, Michael}, booktitle = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, citeulike-article-id = {2369400}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TPAMI.2007.1161}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4359955}, doi = {10.1109/TPAMI.2007.1161}, journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on}, keywords = {image\_feature\_descriptors, image\_processing}, number = {1}, pages = {52--61}, posted-at = {2008-02-13 11:40:03}, priority = {2}, title = {Locally Rotation, Contrast, and Scale Invariant Descriptors for Texture Analysis}, url = {http://dx.doi.org/10.1109/TPAMI.2007.1161}, volume = {30}, year = {2008} }

TY - JOUR ID - citeulike:2369400 L3 - citeulike-article-id:2369400 N2 - Textures within real images vary in brightness, contrast, scale and skew as imaging conditions change. To enable recognition of textures in real images, it is necessary to employ a similarity measure which is invariant to these properties. Furthermore, since textures often appear on undulating surfaces, such invariances must necessarily be local rather than global. Despite these requirements, it is only relatively recently that texture recognition algorithms with local scale and affine invariance properties have begun to be reported. Typically, they comprise detecting feature points followed by geometric normalization prior to description. We describe a method based on invariant combinations of linear filters. Unlike previous methods, we introduce a novel family of filters, which provide scale invariance, resulting in a texture description invariant to local changes in orientation, contrast and scale and robust to local skew. Significantly, the family of filters enable local scale invariants to be defined without using a scale selection principle or a large number of filters. A texture discrimination method based on the Â2 similarity measure applied to histograms derived from our filter responses outperforms existing methods for retrieval and classification results for both the Brodatz textures and the UIUC database, which has been designed to require local invariance. IS - 1 JF - Pattern Analysis and Machine Intelligence, IEEE Transactions on EP - 61 TI - Locally Rotation, Contrast, and Scale Invariant Descriptors for Texture Analysis VL - 30 T2 - Pattern Analysis and Machine Intelligence, IEEE Transactions on SP - 52 KW - image_feature_descriptors KW - image_processing AU - Mellor, Matthew AU - Hong, Byung-Woo AU - Brady, Michael PY - 2008/// UR - http://dx.doi.org/10.1109/TPAMI.2007.1161 ER -