A double circle structure descriptor and Hough voting matching for real-time object detection

In this paper, we propose real-time and reliable approaches for pose tracking of a rigid object by feature detection and image matching. We first present a new fast binary descriptor with a double circle structure of overlapping regions, namely double circle structure descriptor (DCSD). DCSD is rotation invariant and robust against blur, illumination changes, Joint Photographic Experts Group (JPEG) compression and orientation changes. Experimental results show that with fewer feature bits, DCSD is still discriminative and faster than the state-of-the-art features in many general situations. We then propose a new matching measure named Hough Voting Matching (HVM), which is based on clustering and Hough voting schemes. HVM can efficiently discriminate between correct and incorrect keypoint correspondences, and can be combined with some descriptors to improve the matching accuracy as an independent part. Experiments are also presented to illustrate that HVM can refine the matching results of DCSD if we embed HVM into a DCSD algorithm.

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