Random field models in image analysis**
作者:
Richard C. Dubes,
Anil K. Jain,
期刊:
Journal of Applied Statistics
(Taylor Available online 1993)
卷期:
Volume 20,
issue 5-6
页码: 121-154
ISSN:0266-4763
年代: 1993
DOI:10.1080/02664769300000062
出版商: Carfax Publishing Company
数据来源: Taylor
摘要:
Image models are useful in quantitatively specifying natural constraints and general assumptions about the physical world and the imaging process. This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis. Random field models permit the introduction of spatial context into pixel labeling problems, such as segmentation and restoration. Random field models also describe textured images and lead to algorithms for generating textured images, classifying textures and segmenting textured images. In spite of some impressive model-based image restoration and texture segmentation results reported in the literature, a number of fundamental issues remain unexplored, such as the specification of MRF models, modeling noise processes, performance evaluation, parameter estimation, the phase transition phenomenon and the comparative analysis of alternative procedures. The literature of random field models is filled with great promise, but a better mathematical understanding of these issues is needed as well as efficient algorithms for applications. These issues need to be resolved before random field models will be widely accepted as general tools in the image-processing community.
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