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Genetic algorithm is widely applied to the field of image matching. But it is very time-consuming to solve some large computational problems with large search space or complex fitness function. Image matching using genetic algorithm has not achieved real-time performance. An improved genetic algorithm based on CUDA with full use of GPU's parallelism was presented in this paper. The experimental results...
SIFT (Scale Invariant Feature Transform) has proved to be the most robust local invariant feature descriptor in object recognition and matching. Being designed mainly for the gray images, SIFT shows its vulnerability when deal with color images. To overcome this problem and increase the descriptor's distinctiveness, we introduce a new descriptor, a combination of the SIFT approach and the improved...
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