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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.
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Reseña 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.
Biografía del autor
Olivier Collier is a former student of the Ecole Normale Supérieure de Cachan. He obtained his PhD under the supervision of Arnak Dalalyan at Université Paris-Est (France). He is now a postdoctoral researcher at Ecole des Mines de Paris and Institut Curie.
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Statistical Methods for Descriptor Matching
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Statistical Methods for Descriptor Matching
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Scholars' Press Apr 2014, 2014
ISBN 10: 363971539X
ISBN 13: 9783639715392
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783639715392
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Statistical Methods for Descriptor Matching : Mathematical Problems in Computer Vision
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783639715392
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Statistical Methods for Descriptor Matching
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 363971539X
ISBN 13: 9783639715392
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Statistical Methods for Descriptor Matching
Publicado por
Scholars' Press Apr 2014, 2014
ISBN 10: 363971539X
ISBN 13: 9783639715392
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Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Taschenbuch. Condición: 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 Landstraße 99, 66123 Saarbrücken 128 pp. Englisch. Nº de ref. del artículo: 9783639715392
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Statistical Methods for Descriptor Matching
Publicado por
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 363971539X
ISBN 13: 9783639715392
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Librería: Majestic Books, Hounslow, Reino Unido
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Condición: New. Print on Demand pp. 128 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. Nº de ref. del artículo: 131824694
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Statistical Methods for Descriptor Matching
Publicado por
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 363971539X
ISBN 13: 9783639715392
Nuevo
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Condición: New. PRINT ON DEMAND pp. 128. Nº de ref. del artículo: 18128762851
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Statistical Methods for Descriptor Matching: Mathematical Problems in Computer Vision
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paperback. Condición: New. New. book. Nº de ref. del artículo: ERICA829363971539X6
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