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Low Level MRF Models

  • Stan Z. Li
Part of the Computer Science Workbench book series (WORKBENCH)

Abstract

Low level processing is performed on regular lattices of images. The set of sites S = {1,..., m} index image pixels in the image plane. The set L contains continuous or discrete labels for the pixels. Therefore the problems fall into categories LP1 and LP2. Most existing MRF vision models are for low level processing. MRF models for image restoration and segmentation have been studied most comprehensively. Surface reconstruction can be viewed as a more general case than restoration in that the data can be sparse, i.e. available at certain locations of the image lattice. MRFs can play a fuller role in texture analysis because textured images present anisotropic properties. The treatment of optical flow as MRF is similar to that of restoration and reconstruction. Edge detection is often addressed along with restoration, reconstruction and analysis of other image properties such as texture, flow and motion. We can also find low level applications of MRFs such as active contours, deformable templates, data fusion and visual integration.

Keywords

Optical Flow Observation Model Shape Space Gibbs Distribution Texture Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Japan 2001

Authors and Affiliations

  • Stan Z. Li
    • 1
  1. 1.Beijing Sigma CenterMicrosoft Research ChinaBeijingChina

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