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Keypoint Matching Based on Descriptor Statistics
(Izmir Institute of Technology, 2016) Uzyıldırım, Furkan Eren; Uzyıldırım, Furkan Eren; Özuysal, Mustafa; Özuysal, Mustafa; 03.04. Department of Computer Engineering; 01.01. Units Affiliated to the Rectorate; 01. Izmir Institute of Technology; 03. Faculty of Engineering
The binary descriptors are the representation of choice for real-time keypoint matching. However, they suffer from reduced matching rates due to their discrete nature. In this thesis, we propose an approach that can augment their performance by searching in the top K near neighbor matches instead of just the single nearest neighbor one. To pick the correct match out of the K near neighbors, we exploit statistics of descriptor bit variations collected for each keypoint individually in an off-line training phase. This is similar in spirit to approaches that learn a patch specific keypoint representation. Unlike these approaches, we limit the use of a keypoint specific score only to rank the list of K near neighbors. Since this list can be efficiently computed with approximate nearest neighbor algorithms, our approach scales well to large descriptor collections.

Master Degree / Yüksek Lisans Tezleri