Fast image matching for localization in deep-sea based on the simplified SIFT (scale invariant feature transform) algorithm
Li Liu, Fuyuan Peng, Yan Wen Tian, Yiping Xu, Kun Zhao · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Image matching is one of the most important issues in object localization algorithms, while stable feature detection and representation is a fundamental component of many image matching algorithms. SIFT algorithm has been identified as the most resistant feature extraction method to common image deformations. In this paper, we use SSIFT (Simplified Scale Invariant Feature Transform) to solve the problem of image matching in non-structured underwater environments. Like SIFT, we construct a Gaussian pyramid and search for local peaks in a series of difference-of-Gaussian (DOG) images; however, instead of using local square image patch to assign orientation and build 128-element vector, we apply local circle image region and build only 12-element vector for each keypoint. The experiments have shown that SSIFT are more robust to image rotation, and more compact than the standard SIFT representation. We also present fast matching results using such descriptors for non-structured underwater objects.