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» Illumination invariants based on Markov random fields
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ICIP
2003
IEEE
16 years 7 months ago
A probabilistic framework for image segmentation
A new probabilistic image segmentation model based on hypothesis testing and Gibbs Random Fields is introduced. First, a probabilistic difference measure derived from a set of hyp...
Slawo Wesolkowski, Paul W. Fieguth
AAAI
2004
15 years 7 months ago
Interactive Information Extraction with Constrained Conditional Random Fields
Information Extraction methods can be used to automatically "fill-in" database forms from unstructured data such as Web documents or email. State-of-the-art methods have...
Trausti T. Kristjansson, Aron Culotta, Paul A. Vio...
CVPR
2010
IEEE
16 years 2 months ago
Learning to Recognize Shadows in Monochromatic Natural Images
This paper addresses the problem of recognizing shadows from monochromatic natural images. Without chromatic information, shadow classification is very challenging because the in...
Jiejie Zhu, Kegan Samuel, Syed Zain Masood, Marsha...

Publication
281views
17 years 5 months ago
Modeling Image Textures by Gibbs Random Fields
Drawbacks of the traditional scenario of image modeling by Gibbs random fields with multiple pairwise pixel interactions are outlined, and a more reasonable alternative scenario b...
Georgy Gimel'farb
ICIP
2005
IEEE
16 years 7 months ago
Segmenting non stationary images with triplet Markov fields
The hidden Markov field (HMF) model has been used in many model-based solutions to image analysis problems, including that of image segmentation, and generally gives satisfying re...
Dalila Benboudjema, Wojciech Pieczynski