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CVPR
2007
IEEE
14 years 7 months ago
Adaptive Patch Features for Object Class Recognition with Learned Hierarchical Models
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
Fabien Scalzo, Justus H. Piater
ICCV
2007
IEEE
13 years 12 months ago
Support Kernel Machines for Object Recognition
Kernel classifiers based on Support Vector Machines (SVM) have recently achieved state-of-the art results on several popular datasets like Caltech or Pascal. This was possible by...
Ankita Kumar, Cristian Sminchisescu
IROS
2008
IEEE
113views Robotics» more  IROS 2008»
13 years 12 months ago
Motion recognition and generation by combining reference-point-dependent probabilistic models
— This paper presents a method to recognize and generate sequential motions for object manipulation such as placing one object on another or rotating it. Motions are learned usin...
Komei Sugiura, Naoto Iwahashi
CVPR
2005
IEEE
14 years 7 months ago
Combining Object and Feature Dynamics in Probabilistic Tracking
Objects can exhibit different dynamics at different scales, and this is often exploited by visual tracking algorithms. A local dynamic model is typically used to extract image fea...
Leonid Taycher, John W. Fisher III, Trevor Darrell
ECCV
2010
Springer
13 years 5 months ago
Object Recognition with Hierarchical Stel Models
Abstract. We propose a new generative model, and a new image similarity kernel based on a linked hierarchy of probabilistic segmentations. The model is used to efficiently segment ...
Alessandro Perina, Nebojsa Jojic, Umberto Castella...