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» Supervised Image Segmentation Using Markov Random Fields
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ECCV
2006
Springer
15 years 11 months ago
Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics
Abstract. This paper presents a novel approach to unsupervised texture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method...
Suyash P. Awate, Tolga Tasdizen, Ross T. Whitaker
ICIP
2005
IEEE
15 years 11 months ago
Joint feature-spatial-measure space: a new approach to highly efficient probabilistic object tracking
In this paper we present a probabilistic framework for tracking objects based on local dynamic segmentation. We view the segn to be a Markov labeling process and abstract it as a ...
Feng Chen, XiaoTong Yuan, ShuTang Yang
CVPR
2010
IEEE
15 years 6 months ago
Segmenting Video Into Classes of Algorithm-Suitability
Given a set of algorithms, which one(s) should you apply to, i) compute optical flow, or ii) perform feature matching? Would looking at the sequence in question help you decide? I...
Oisin Mac Aodha, Gabriel Brostow, marc Pollefeys
82
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WACV
2002
IEEE
15 years 2 months ago
Range Synthesis for 3D Environment Modeling
In this paper a range synthesis algorithm is proposed as an initial solution to the problem of 3D environment modeling from sparse data. We develop a statistical learning method f...
Luz Abril Torres-Méndez, Gregory Dudek

Source Code
445views
15 years 10 months ago
Blocks
Blocks is an open source modular MATLAB framework which allows the user to avoid needlessly repeating computation. Blocks may be easily used for your own experiments, and comes ...
Brian Fulkerson, Andrea Vedaldi