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» Markov Random Field Modeling in Computer Vision
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SIAMIS
2010
141views more  SIAMIS 2010»
13 years 3 months ago
Optimization by Stochastic Continuation
Simulated annealing (SA) and deterministic continuation are well-known generic approaches to global optimization. Deterministic continuation is computationally attractive but produ...
Marc C. Robini, Isabelle E. Magnin
MVA
2007
179views Computer Vision» more  MVA 2007»
13 years 8 months ago
Multi-object trajectory tracking
The majority of existing tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework using a Hidden Markov Model, where the distribution ...
Mei Han, Wei Xu, Hai Tao, Yihong Gong
CVPR
1998
IEEE
14 years 11 months ago
Action Recognition Using Probabilistic Parsing
A new approach to the recognition of temporal behaviors and activities is presented. The fundamental idea, inspired by work in speech recognition, is to divide the inference probl...
Aaron F. Bobick, Yuri A. Ivanov
ICCV
2003
IEEE
14 years 11 months ago
Automatically Labeling Video Data Using Multi-class Active Learning
Labeling video data is an essential prerequisite for many vision applications that depend on training data, such as visual information retrieval, object recognition, and human act...
Rong Yan, Jie Yang, Alexander G. Hauptmann
CVPR
2008
IEEE
14 years 11 months ago
A mixed generative-discriminative framework for pedestrian classification
This paper presents a novel approach to pedestrian classification which involves utilizing the synthesized virtual samples of a learned generative model to enhance the classificat...
Markus Enzweiler, Dariu M. Gavrila