Statistical Methods for Descriptor Matching

Collier, Olivier

ISBN 10: 363971539X ISBN 13: 9783639715392

Editorial: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014

Idioma: Ingl�s

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Descripci�n

pp. 128.

N� de ref. del art�culo 26128762857

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T�tulo: Statistical Methods for Descriptor Matching
Editor: VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG
Fecha de publicaci�n: 2014
Idioma: Ingl�s
ISBN 10: 363971539X
ISBN 13: 9783639715392
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

Many applications, as in computer vision or medicine, aim at identifying the similarities between several images or signals. Thereafter, it is possible to detect objects, to follow them, or to overlap different pictures. In every case, the algorithmic procedures that treat the images use a selection of keypoints that they try to match by pairs. The most popular algorithm nowadays is SIFT, that performs keypoint selection, descriptor calculation, and provides a criterion for global descriptor matching. We considered changing the classical descriptor, which resulted in a shift testing problem that we solved in the minimax frame. Then, we gave a rigorous statistical formulation for the global descriptor matching problem and studied it in some special cases.

Rese�a del editor

Many applications, as in computer vision or medicine, aim at identifying the similarities between several images or signals. Thereafter, it is possible to detect objects, to follow them, or to overlap different pictures. In every case, the algorithmic procedures that treat the images use a selection of keypoints that they try to match by pairs. The most popular algorithm nowadays is SIFT, that performs keypoint selection, descriptor calculation, and provides a criterion for global descriptor matching. We considered changing the classical descriptor, which resulted in a shift testing problem that we solved in the minimax frame. Then, we gave a rigorous statistical formulation for the global descriptor matching problem and studied it in some special cases.

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