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» Markov Random Field Modeling in Computer Vision
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CVPR
2008
IEEE
14 years 11 months ago
Principled fusion of high-level model and low-level cues for motion segmentation
High-level generative models provide elegant descriptions of videos and are commonly used as the inference framework in many unsupervised motion segmentation schemes. However, app...
Arasanathan Thayananthan, Masahiro Iwasaki, Robert...
CVPR
2012
IEEE
11 years 11 months ago
Top-down and bottom-up cues for scene text recognition
Scene text recognition has gained significant attention from the computer vision community in recent years. Recognizing such text is a challenging problem, even more so than the ...
Anand Mishra, Karteek Alahari, C. V. Jawahar
CVPR
2012
IEEE
11 years 11 months ago
A learning-based framework for depth ordering
Depth ordering is instrumental for understanding the 3D geometry of an image. We as humans are surprisingly good ordering even with abstract 2D line drawings. In this paper we pro...
Zhaoyin Jia, Andrew C. Gallagher, Yao-Jen Chang, T...
ICIC
2005
Springer
14 years 2 months ago
Sequential Stratified Sampling Belief Propagation for Multiple Targets Tracking
Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number...
Jianru Xue, Nanning Zheng, Xiaopin Zhong
CGF
2005
167views more  CGF 2005»
13 years 9 months ago
Adaptive Deformable Models for Graphics and Vision
Deformable models are a powerful tool in both computer graphics and computer vision. The description and implementation of the deformations have to be simultaneously flexible and ...
Siome Goldenstein, Christian Vogler, Luiz Velho