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Erschienen in:
2014 | OriginalPaper | Buchkapitel
CS-FREAK: An Improved Binary Descriptor
verfasst von : Jianyong Wang, Xuemei Wang, Xiaogang Yang, Aigang Zhao
Erschienen in: Advances in Image and Graphics Technologies
Verlag: Springer Berlin Heidelberg
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A large number of vision applications rely on matching key points across images, its main problem is to find a fast and robust key point descriptor and a matching strategy. This paper presents a two-step matching strategy based on voting and an improved binary descriptor CS-FREAK by adding the neighborhood intensity information of the sampling points to the FREAK descriptor. This method divides the matching task into two steps, firstly simplify the FREAK[1] 8-layer retina model to a 5-layer one and construct a binary descriptor, secondly encode the neighborhood intensity information of the center symmetry sampling points, and then create a 16-dimentional histogram according to a pre-constructed index table, which is the basis for voting strategy. This two-step matching strategy can improve learning efficiency meanwhile enhance the descriptor identification ability, and improve the matching accuracy. Experimental results show that the accuracy of the matching method is superior to SIFT and FREAK.