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CVPR
2008
IEEE
14 years 9 months ago
Object recognition and segmentation by non-rigid quasi-dense matching
In this paper, we present a non-rigid quasi-dense matching method and its application to object recognition and segmentation. The matching method is based on the match propagation...
Esa Rahtu, Janne Heikkilä, Juho Kannala, Sami...
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
CVPR
1998
IEEE
14 years 9 months ago
The Sample Tree: A Sequential Hypothesis Testing Approach to 3D Object Recognition
A method is presented for e cient and reliable object recognition within noisy, cluttered, and occluded range images. The method is based on a strategy which hypothesizes the inte...
Michael A. Greenspan
CVPR
2000
IEEE
13 years 12 months ago
Adaptive Bayesian Recognition in Tracking Rigid Objects
We present a framework for tracking rigid objects based on an adaptive Bayesian recognition technique that incorporates dependencies between object features. At each frame we fin...
Yuri Boykov, Daniel P. Huttenlocher
IJON
2002
120views more  IJON 2002»
13 years 7 months ago
The recognition and analysis of animate objects using neural networks and active contour models
: In this paper we describe a method for tracking walking humans in the visual field. Active contour models are used to track moving objects in a sequence of images. The resulting ...
Ken Tabb, Neil Davey, Rod Adams, Stella J. George