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» Randomness and Geometric Features in Computer Vision
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WACV
2007
IEEE
14 years 1 months ago
Extraction of Person Silhouettes from Surveillance Imagery using MRFs
We present a method for the simultaneous detection and segmentation of objects from static images. We employ lowlevel contour features that enable us to learn the coarse object sh...
Vinay Sharma, James W. Davis
CVPR
2009
IEEE
15 years 2 months ago
Discriminative Structure Learning of Hierarchical Representations for Object Detection
A variety of flexible models have been proposed to detect objects in challenging real world scenes. Motivated by some of the most successful techniques, we propose a hierarchica...
Paul Schnitzspan (TU Darmstadt), Mario Fritz (Univ...
CVPR
2007
IEEE
14 years 9 months ago
Simultaneous Detection and Segmentation of Pedestrians using Top-down and Bottom-up Processing
We present a method for the simultaneous detection and segmentation of people from static images. The proposed technique requires no manual segmentation during training, and explo...
Vinay Sharma, James W. Davis
ICPR
2008
IEEE
14 years 8 months ago
A novel Gaussianized vector representation for natural scene categorization
This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of featur...
Hao Tang, Mark Hasegawa-Johnson, Thomas S. Huang, ...
CVPR
1999
IEEE
13 years 12 months ago
A New Bayesian Framework for Object Recognition
We introduce an approach to feature-based object recognition, using maximum a posteriori (MAP) estimation under a Markov random field (MRF) model. This approach provides an effici...
Yuri Boykov, Daniel P. Huttenlocher