A Fast Image SIFT Feature Matching Method Based on GPU and Cascade Hash
A feature matching and image technology, applied in the field of computer vision, can solve problems such as a large amount of computing time, and achieve the effect of shortening matching time and powerful parallel computing capabilities
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[0029] Such as figure 1 Shown is the overall flow diagram of the method of the embodiment of the present invention. From figure 1 It can be seen that this method includes the establishment of a hard disk, memory, and GPU three-level exchange mechanism, the upper triangle block reading method, a rough hash screening module, and a fine hash screening and output module. Its specific implementation is as follows:
[0030] (1) According to the GPU global video memory size limit and memory size limit, all image feature point data are divided into blocks and grouped, and a three-level exchange mechanism of GPU, memory, and hard disk is established at the computer level;
[0031] (2) Propose and use an improved GPU parallel protocol method, make full use of the three-level cache mechanism of global video memory, shared memory, and registers at the internal level of the GPU, and perform two hash maps with different encoding lengths on all SIFT feature points of the image;
[0032] (...
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