Keypoint matching based on descriptor statistics için kapak resmi
Keypoint matching based on descriptor statistics
Keypoint matching based on descriptor statistics
Uzyıldırım, Furkan Eren, author.
Fiziksel Tanımlama:
x, 106 leaves: color illustraltions.+ 1 computer laser optical disc.
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.
Yazar Ek Girişi:
Tek Biçim Eser Adı:
Thesis (Master)--İzmir Institute of Technology: Computer Engineering.

İzmir Institute of Technology: Computer Engineering--Thesis (Master).
Elektronik Erişim:
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