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Markov Random Field Modeling in Computer Vision,
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260 pp.
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HTML Version. Or:
HTML Version. Markov random field (MRF) theory provides a basis for modeling contextual
constraints in visual processing and interpretation.
Topics include:
introduction to fundamental theories, formulations
of MRF vision models, MRF parameter estimation, and optimization algorithms.
Various vision models are presented in a unified framework, including image
restoration and reconstruction, edge and region segmentation, texture, stereo
and motion, object matching and recognition, and pose estimation.
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Discriminative random fields: a discriminative framework for contextual
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Classify regions given a single image.
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Assigning labels to the nodes of a graphical model such
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Graph Cut Based Inference with Co-occurrence Statistics,
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Associative hierarchical CRFs for object class image segmentation,
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Compute segmentations (or labellings) at different levels, pixels, segments,
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Ladicky, L.[Lubor],
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Computational modeling
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Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Structural Matching for Computer Vision .