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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Description of Interest Regions with Center-Symmetric Local Binary Patterns
verfasst von : Marko Heikkilä, Matti Pietikäinen, Cordelia Schmid
Erschienen in: Computer Vision, Graphics and Image Processing
Verlag: Springer Berlin Heidelberg
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Local feature detection and description have gained a lot of interest in recent years since photometric descriptors computed for interest regions have proven to be very successful in many applications. In this paper, we propose a novel interest region descriptor which combines the strengths of the well-known SIFT descriptor and the LBP texture operator. It is called the
center-symmetric local binary pattern (CS-LBP) descriptor
. This new descriptor has several advantages such as tolerance to illumination changes, robustness on flat image areas, and computational efficiency. We evaluate our descriptor using a recently presented test protocol. Experimental results show that the CS-LBP descriptor outperforms the SIFT descriptor for most of the test cases, especially for images with severe illumination variations.