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ICCV
2009
IEEE
17 years 13 days ago
A Global Perspective on MAP Inference for Low-Level Vision
In recent years the Markov Random Field (MRF) has become the de facto probabilistic model for low-level vision applications. However, in a maximum a posteriori (MAP) framework, ...
Oliver J. Woodford, Carsten Rother, Vladimir Kolmo...
ICCV
2009
IEEE
1068views Computer Vision» more  ICCV 2009»
17 years 13 days ago
Illumination Aware MCMC Particle Filter for Long-Term Outdoor Multi-Object Simultaneous Tracking and Classification
This paper addresses real-time automatic visual tracking, labeling and classification of a variable number of objects such as pedestrians or/and vehicles, under timevarying illu...
Franc¸ois Bardet, Thierry Chateau, Datta Ramadasa...
230
Voted
CVPR
1998
IEEE
16 years 9 months ago
Histogram Model for 3D Textures
Image texture can arise not only from surface albedo variations (2D texture) but also from surface height variations (3D texture). Since the appearance of 3D texture depends on th...
Kristin J. Dana, Shree K. Nayar
CVPR
2003
IEEE
16 years 9 months ago
Object Recognition Based on Photometric Alignment Using RANSAC
For object recognition under varying illumination conditions, we propose a method based on photometric alignment. The photometric alignment is known as a technique that models bot...
Takahiro Okabe, Yoichi Sato
229
Voted
CVPR
2004
IEEE
16 years 9 months ago
Efficient Belief Propagation for Early Vision
Markov random field models provide a robust and unified framework for early vision problems such as stereo, optical flow and image restoration. Inference algorithms based on graph...
Pedro F. Felzenszwalb, Daniel P. Huttenlocher