Keypoint matching based on descriptor statistics
tarafından
 
Uzyıldırım, Furkan Eren, author.

Başlık
Keypoint matching based on descriptor statistics

Yazar
Uzyıldırım, Furkan Eren, author.

Yazar Ek Girişi
Uzyıldırım, Furkan Eren, author.

Fiziksel Tanımlama
x, 106 leaves: color illustraltions.+ 1 computer laser optical disc.

Özet
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.

Konu Başlığı
Pattern recognition systems.
 
Computer algorithms.
 
Computer vision.

Yazar Ek Girişi
Özuysal, Mustafa

Tüzel Kişi Ek Girişi
İzmir Institute of Technology. Computer Engineering.

Tek Biçim Eser Adı
Thesis (Master)--İzmir Institute of Technology: Computer Engineering.
 
İzmir Institute of Technology: Computer Engineering--Thesis (Master).

Elektronik Erişim
Access to Electronic Versiyon.


LibraryMateryal TürüDemirbaş NumarasıYer Numarası
IYTE LibraryTezT001491TK7882.P3 U99 2016