Object Level Grouping for Video Shots
International Journal of Computer Vision
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Modeling 3d objects from stereo views and recognizing them in photographs
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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This thesis introduces a novel representation for three-dimensional (3D) objects in terms of local affine-invariant descriptors of their appearance and the spatial relationships between the corresponding affine regions. Geometric constraints associated with different views of the same surface patches are combined with a normalized representation of their appearance to guide matching and reconstruction, allowing the acquisition of true 3D models from multiple unregistered images, as well as their recognition in photographs and image sequences. The proposed approach is applied to two domains: (1) Photographs—Models of rigid objects are constructed from photos and recognized in highly cluttered shots taken from arbitrary viewpoints. (2) Video—Dynamic scenes containing multiple moving objects observed by a moving camera are segmented into rigid components, and the 3D models constructed from these components are matched across different image sequences, with application to shot matching.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Windows MediaPlayer or RealPlayer.