3D object modeling and recognition from photographs and image sequences

Fred Rothganger 1 Svetlana Lazebnik 1 Cordelia Schmid 2, * Jean Ponce 1
* Auteur correspondant
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : This chapter proposes a representation of rigid threedimensional (3D) objects in terms of local affine-invariant descriptors of their images and the spatial relationships between the corresponding surface patches. Geometric constraints associated with different views of the same patches under affine projection are combined with a normalized representation of their appearance to guide the matching process involved in object modeling and recognition tasks. The proposed approach is applied in two domains: (1) Photographs — models of rigid objects are constructed from small sets of images and recognized in highly cluttered shots taken from arbitrary viewpoints. (2) Video — dynamic scenes containing multiple moving objects are segmented into rigid components, and the resulting 3D models are directly matched to each other, giving a novel approach to video indexing and retrieval.
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Chapitre d'ouvrage
Jean Ponce and Martial Hebert and Cordelia Schmid and Andrew Zisserman. Towards category-Level object recognition, 4170, Springer-Verlag, pp.105--126, 2006, Lecture Notes in Computer Science (LNCS), 978-3-540-68794-8
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Fred Rothganger, Svetlana Lazebnik, Cordelia Schmid, Jean Ponce. 3D object modeling and recognition from photographs and image sequences. Jean Ponce and Martial Hebert and Cordelia Schmid and Andrew Zisserman. Towards category-Level object recognition, 4170, Springer-Verlag, pp.105--126, 2006, Lecture Notes in Computer Science (LNCS), 978-3-540-68794-8. 〈inria-00548594〉

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