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In the area of computer vision, pattern recognition and image processing, image match is a research hotspot with important theoretical significance and practical value. Recently, the image matching algorithm based on SIFT has drown wide attention for its outstanding local feature matching performance. In view of high computation complexity, poor anti-noise ability, and difficulty for practical use...
Image matching is at the base of many computer vision problems, such as object recognition or image stitching. Standard SIFT provides poor performance when images under viewpoint change conditions and with similar corners. Hence, we propose a matching algorithm combine regional SSDA with simplified SIFT algorithm. We demonstrate through experiments that our algorithm yields better performance in images...
This paper proposes a relative shape context and relaxation labeling (RSC-RL) based approach for point pattern matching (PPM). First of all, a new point set based invariant feature, Relative Shape Context (RSC), is proposed. Using the test statistic of relative shape context descriptor's matching scores as the foundation of support function, the point pattern matching probability matrix can be iteratively...
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