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» Markov Random Field Modeling in Computer Vision
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CVPR
2009
IEEE
1133views Computer Vision» more  CVPR 2009»
15 years 4 months ago
Hierarchical Spatio-Temporal Context Modeling for Action Recognition
The problem of recognizing actions in realistic videos is challenging yet absorbing owing to its great potentials in many practical applications. Most previous research is limit...
Jintao Li, Ju Sun, Loong Fah Cheong, Shuicheng Yan...
ICPR
2004
IEEE
14 years 10 months ago
Robust Playfield Segmentation using MAP Adaptation
A vital task in sports video annotation is to detect and segment areas of the playfield. This is an important first step in player or ball tracking and detecting the location of t...
Jean-Marc Odobez, Mark Barnard
CVPR
2009
IEEE
1148views Computer Vision» more  CVPR 2009»
15 years 4 months ago
Shape Constrained Figure-Ground Segmentation and Tracking
Global shape information is an effective top-down complement to bottom-up figure-ground segmentation as well as a useful constraint to avoid drift during adaptive tracking. We p...
Zhaozheng Yin, Robert T. Collins
CVPR
2004
IEEE
14 years 11 months ago
A Variational Approach to Scene Reconstruction and Image Segmentation from Motion-Blur Cues
In this paper we are interested in the joint reconstruction of geometry and photometry of scenes with multiple moving objects from a collection of motion-blurred images. We make s...
Paolo Favaro, Stefano Soatto
IPSN
2004
Springer
14 years 2 months ago
Estimation from lossy sensor data: jump linear modeling and Kalman filtering
Due to constraints in cost, power, and communication, losses often arise in large sensor networks. The sensor can be modeled as an output of a linear stochastic system with random...
Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal