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CVPR
2005
IEEE
14 years 9 months ago
Hybrid Models for Human Motion Recognition
Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the h...
Claudio Fanti, Lihi Zelnik-Manor, Pietro Perona
CVPR
2007
IEEE
14 years 9 months ago
Accurate Object Detection with Deformable Shape Models Learnt from Images
We present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models dire...
Cordelia Schmid, Frédéric Jurie, Vit...
CVPR
2008
IEEE
14 years 9 months ago
Local deformation models for monocular 3D shape recovery
Without a deformation model, monocular 3D shape recovery of deformable surfaces is severly under-constrained. Even when the image information is rich enough, prior knowledge of th...
Mathieu Salzmann, Raquel Urtasun, Pascal Fua
BMVC
2001
13 years 10 months ago
Learning Pixel-Wise Signal Energy for Understanding Semantics
Visual interpretation of events requires both an appropriate representation of change occurring in the scene and the application of semantics for differentiating between different...
Jeffrey Ng, Shaogang Gong
ECCV
2004
Springer
14 years 9 months ago
Recognition by Probabilistic Hypothesis Construction
We present a probabilistic framework for recognizing objects in images of cluttered scenes. Hundreds of objects may be considered and searched in parallel. Each object is learned f...
Pierre Moreels, Michael Maire, Pietro Perona