Statistical Methods for Descriptor Matching: Mathematical Problems in Computer Vision

Collier, Olivier

ISBN 10: 363971539X / ISBN 13: 9783639715392

Editore: Scholars* Press, 2014

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Codice articolo ERICA829363971539X6

Dati bibliografici

• Titolo: Statistical Methods for Descriptor Matching: Mathematical Problems in Computer Vision
• Editore: Scholars* Press
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 363971539X
• ISBN 13: 9783639715392
• Rilegatura: paperback
• Condizione: New
• Tipo di libro: book

Descrizione articolo

Riassunto

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.

L'autore

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.