Local feature detection and description have gained a lot of interest in recent years since photometric descriptors computed for in- terest 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 computa- tional 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.
CITATION STYLE
Sharma, N., Pal, U., Kimura, F., & Pal, S. (2006). Recognition of Off-Line Handwritten Devnagari Characters Using Quadratic Classifier (pp. 805–816). https://doi.org/10.1007/11949619_72
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