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Proceedings Paper

Detection of the number of image regions by minimum bias/variance criterion
Author(s): Yue Joseph Wang; Tianhu Lei; Joel M. Morris
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Paper Abstract

An unsupervised stochastic model-based image analysis technique requires the model parameters to be estimated directly from the observed image. A new approach is presented to the problem of detecting the number of statistically distinct regions in an image, based on the application of a new information theoretic criterion called minimum bias/variance criterion (MBVC). Different from the conventional approximation and coding based approaches introduced by Akaike and by Rissanen, the new criterion is to reformulate the problem explicitly as a problem of model bias and variance balancing in which the number of image regions is obtained merely by minimizing the MBVC value. Simulation results that illustrate the performance of the new method for the detection of the number of regions in an image are presented with both synthetic and medical images.

Paper Details

Date Published: 16 September 1994
PDF: 10 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.185958
Show Author Affiliations
Yue Joseph Wang, Univ. of Maryland/Baltimore County (United States)
Tianhu Lei, Univ. of Maryland/Baltimore (United States)
Joel M. Morris, Univ. of Maryland/Baltimore County (United States)


Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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