Statistical Methods for Descriptor Matching: Mathematical Problems in Computer Vision - Couverture souple

Collier, Olivier

9783639715392: Statistical Methods for Descriptor Matching: Mathematical Problems in Computer Vision

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ISBN 10 : 363971539X ISBN 13 : 9783639715392

Editeur : Scholars' Press, 2014

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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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Pr�sentation de l'�diteur

Biographie de l'auteur

Olivier Collier is a former student of the Ecole Normale Sup�rieure de Cachan. He obtained his PhD under the supervision of Arnak Dalalyan at Universit� Paris-Est (France). He is now a postdoctoral researcher at Ecole des Mines de Paris and Institut Curie.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteurScholars' Press
Date d'�dition2014
ISBN 10 363971539X
ISBN 13 9783639715392
ReliureBroch�
Langueanglais
Nombre de pages128
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Statistical Methods for Descriptor Matching

Olivier Collier

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Statistical Methods for Descriptor Matching : Mathematical Problems in Computer Vision

Olivier Collier

Edit� par Scholars' Press, 2014

ISBN 10 : 363971539X ISBN 13 : 9783639715392

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. N� de r�f. du vendeur 9783639715392

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Statistical Methods for Descriptor Matching

Olivier Collier

Edit� par Scholars' Press Apr 2014, 2014

ISBN 10 : 363971539X ISBN 13 : 9783639715392

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 128 pp. Englisch. N� de r�f. du vendeur 9783639715392

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Statistical Methods for Descriptor Matching

Olivier Collier

Edit� par Scholars' Press Apr 2014, 2014

ISBN 10 : 363971539X ISBN 13 : 9783639715392

Neuf Taschenbuch

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. Neuware -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.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 128 pp. Englisch. N� de r�f. du vendeur 9783639715392

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