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» Supervised Image Segmentation Using Markov Random Fields
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ICIP
2000
IEEE
16 years 1 months ago
Definition of a Spatial Entropy and its Use for Texture Discrimination
This paper presents a new definition of a spatial entropy mainly based on the Markov Random Field (MRF) properties. Starting with the study of the entropy proposed in [1] for the ...
Florence Tupin, Henri Maître, Marc Sigelle
ICML
2010
IEEE
15 years 27 days ago
Conditional Topic Random Fields
Generative topic models such as LDA are limited by their inability to utilize nontrivial input features to enhance their performance, and many topic models assume that topic assig...
Jun Zhu, Eric P. Xing
ICIP
2010
IEEE
14 years 9 months ago
View synthesis based on Conditional Random Fields and graph cuts
We propose a novel method to synthesize intermediate views from two stereo images and disparity maps that is robust to errors in disparity map. The proposed method computes a plac...
Lam C. Tran, Christopher J. Pal, Truong Q. Nguyen
ECCV
2004
Springer
16 years 1 months ago
MCMC-Based Multiview Reconstruction of Piecewise Smooth Subdivision Curves with a Variable Number of Control Points
We investigate the automated reconstruction of piecewise smooth 3D curves, using subdivision curves as a simple but flexible curve representation. This representation allows taggin...
Michael Kaess, Rafal Zboinski, Frank Dellaert
ECCV
2006
Springer
16 years 1 months ago
Learning to Combine Bottom-Up and Top-Down Segmentation
Bottom-up segmentation based only on low-level cues is a notoriously difficult problem. This difficulty has lead to recent top-down segmentation algorithms that are based on class-...
Anat Levin, Yair Weiss