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» Spatio-Temporal Markov Random Field for Video Denoising
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ICMCS
2008
IEEE
211views Multimedia» more  ICMCS 2008»
14 years 1 months ago
A real-time video surveillance system with human occlusion handling using nonlinear regression
This paper presents a real-time single-camera surveillance system, aiming at detecting and partly analyzing a group of people. A set of moving persons is segmented using a combina...
Jungong Han, Minwei Feng, Peter H. N. de With
BMVC
2010
13 years 5 months ago
Motion Coherent Tracking with Multi-label MRF optimization
We present a novel off-line algorithm for target segmentation and tracking in video. In our approach, video data is represented by a multi-label Markov Random Field model, and seg...
David Tsai, Matthew Flagg, James M. Rehg
TSP
2008
151views more  TSP 2008»
13 years 7 months ago
Convergence Analysis of Reweighted Sum-Product Algorithms
Markov random fields are designed to represent structured dependencies among large collections of random variables, and are well-suited to capture the structure of real-world sign...
Tanya Roosta, Martin J. Wainwright, Shankar S. Sas...
TIP
2010
161views more  TIP 2010»
13 years 2 months ago
Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content
Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, ...
Xiang Zhu, Peyman Milanfar
ICCV
2009
IEEE
15 years 13 days ago
Higher-Order Gradient Descent by Fusion-Move Graph Cut
Markov Random Field is now ubiquitous in many formulations of various vision problems. Recently, optimization of higher-order potentials became practical using higherorder graph...
Hiroshi Ishikawa