A class of discrete multiresolution random fields and its application to image segmentation
UNSPECIFIED. (2003) A class of discrete multiresolution random fields and its application to image segmentation. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 25 (1). pp. 42-56. ISSN 0162-8828Full text not available from this repository.
In this paper, a class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images. The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant variation of properties within each region. The estimation algorithms used are stochastic, but because of the multiresolution representation, are fast computationally, requiring only a few iterations per pixel to converge to accurate results, with error rates of 1-2 percent across a range of image structures and textures. The addition of a simple boundary process gives accurate results even at low resolutions, and consequently at very low computational cost.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
|Journal or Publication Title:||IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE|
|Publisher:||IEEE COMPUTER SOC|
|Number of Pages:||15|
|Page Range:||pp. 42-56|
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