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CVPR
2006
IEEE
14 years 1 months ago
Depth from Familiar Objects: A Hierarchical Model for 3D Scenes
We develop an integrated, probabilistic model for the appearance and three-dimensional geometry of cluttered scenes. Object categories are modeled via distributions over the 3D lo...
Erik B. Sudderth, Antonio B. Torralba, William T. ...
CVPR
2009
IEEE
15 years 2 months ago
Constrained Marginal Space Learning for Efficient 3D Anatomical Structure Detection in Medical Images
Recently, we proposed marginal space learning (MSL) as a generic approach for automatic detection of 3D anatom- ical structures in many medical imaging modalities. To accurately...
Yefeng Zheng, Bogdan Georgescu, Haibin Ling, Shaoh...
ECCV
2008
Springer
14 years 9 months ago
Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features
Abstract. Recently, impressive results have been reported for the detection of objects in challenging real-world scenes. Interestingly however, the underlying models vary greatly e...
Paul Schnitzspan, Mario Fritz, Bernt Schiele
CVPR
2012
IEEE
11 years 10 months ago
SURFing the point clouds: Selective 3D spatial pyramids for category-level object recognition
This paper proposes a novel approach to recognize object categories in point clouds. By quantizing 3D SURF local descriptors, computed on partial 3D shapes extracted from the poin...
Carolina Redondo-Cabrera, Roberto Javier Ló...
CVPR
2011
IEEE
13 years 4 months ago
Learning to Recognize Objects in Egocentric Activities
This paper addresses the problem of learning object models from egocentric video of household activities, using extremely weak supervision. For each activity sequence, we know onl...
Alireza Fathi, Xiaofeng Ren, James Rehg