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» Boosting Chain Learning for Object Detection
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UAI
1996
13 years 8 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
PAMI
2006
193views more  PAMI 2006»
13 years 7 months ago
A System for Learning Statistical Motion Patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, w...
Weiming Hu, Xuejuan Xiao, Zhouyu Fu, Dan Xie, Tien...
CVPR
2008
IEEE
14 years 9 months ago
Discovering class specific composite features through discriminative sampling with Swendsen-Wang Cut
This paper proposes a novel approach to discover a set of class specific "composite features" as the feature pool for the detection and classification of complex objects...
Feng Han, Ying Shan, Harpreet S. Sawhney, Rakesh K...
CVPR
2004
IEEE
14 years 9 months ago
Automatic Cascade Training with Perturbation Bias
Face detection methods based on a cascade architecture have demonstrated fast and robust performance. Cascade learning is aided by the modularity of the architecture in which node...
Jie Sun, James M. Rehg, Aaron F. Bobick
ICIP
2008
IEEE
14 years 9 months ago
Implicit spatial inference with sparse local features
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-clas...
Deirdre O'Regan, Anil C. Kokaram