Abstract
This letter improves the computational efficiency and proposes a methodology to retrieve the missing feature pairs when utilizing binary-based features for image matching. The 64-byte feature descriptors of binary robust invariant scalable keypoints (BRISKs) are rearranged by combining human retina ganglion cells distribution and visual accommodation to speed up the image matching. In addition, an interactive two-sided matching is designed to determine the most probable keypoint pair when a feature point in the reference image is mapped to multiple candidates in the target image. Experimental results indicate that the proposed inverse sorting ring can reduce the processing time by more than 10% compared to the accelerated BRISK while maintaining the same reliability. Also, additional point-to-point feature pairs can be regained from the point-to-multicandidate cases by the proposed method in order to increase the number of matches.
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