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APPROX
2008
Springer
119views Algorithms» more  APPROX 2008»
14 years 27 days ago
The Complexity of Distinguishing Markov Random Fields
Abstract. Markov random fields are often used to model high dimensional distributions in a number of applied areas. A number of recent papers have studied the problem of reconstruc...
Andrej Bogdanov, Elchanan Mossel, Salil P. Vadhan
ICIP
2009
IEEE
14 years 10 months ago
A Markov Random Field Model for Extracting Near-Circular Shapes
We propose a binary Markov Random Field (MRF) model that assigns high probability to regions in the image domain consisting of an unknown number of circles of a given radius. We...
Tamas Blaskovics, Zoltan Kato, and Ian Jermyn
ICMCS
2009
IEEE
415views Multimedia» more  ICMCS 2009»
13 years 8 months ago
A new localized superpixel Markov random field for image segmentation
In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel label...
Xiaofeng Wang, Xiao-Ping Zhang
ICPR
2002
IEEE
14 years 12 months ago
Robust Appearance-Based Object Recognition Using a Fully Connected Markov Random Field
This paper presents a new kernel method for appearance-based object recognition, highly robust to noise and occlusion. It consists of a fully connected Markov Random Field that in...
Barbara Caputo, Sahla Bouattour, Heinrich Niemann
EMMCVPR
1999
Springer
14 years 3 months ago
Auxiliary Variables for Markov Random Fields with Higher Order Interactions
Markov Random Fields are widely used in many image processing applications. Recently the shortcomings of some of the simpler forms of these models have become apparent, and models ...
Robin D. Morris