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Content Provider | IEEE Xplore Digital Library |
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Author | Ojala, T. Pietikainen, M. Maenpaa, T. |
Copyright Year | 1979 |
Abstract | Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns. |
Sponsorship | IEEE Computer Society |
Starting Page | 971 |
Ending Page | 987 |
Page Count | 17 |
File Size | 3239774 |
File Format | |
ISSN | 01628828 |
Volume Number | 24 |
Issue Number | 7 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2002-07-01 |
Publisher Place | U.S.A. |
Access Restriction | One Nation One Subscription (ONOS) |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Gray-scale Spatial resolution Prototypes Pattern recognition Image recognition Image texture Histograms Quantization Multiresolution analysis Robustness |
Content Type | Text |
Resource Type | Article |
Subject | Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software |
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