5 July 1995 Feature estimation and object extraction using Markov random field modeling
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Abstract
Automatic object recognition requirements exist to extract objects of interest from cluttered background. Most of the feature estimation and object extraction algorithms are designed to extract the known geometry of objects of interest. Recently, Markov random field modeling has been found useful in characterizing the clutter and the man-made objects. In this paper, a new approach to estimate features, particularly the first and second moments, is proposed to single out the most 'smooth' region in an image. The approach exploits the characteristics of man-made objects and background clutter characterized with natural scene using Markov random field modeling. Regions-of-interest are clustered and differentiated in terms of the features estimated at pixel level. Experiments have been conducted on different types of imagery such as camera, IR, and ladar. The results show that the algorithms work particularly well to extract man-made objects versus natural scene. Requiring no prior knowledge about the objects except that they can be characterized with relative 'smooth' surface, the algorithm is suitable for tracking an object moving in field. It is also useful for low/intermediate level processing for model-based pattern recognition systems.
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Byron H. Chen, Byron H. Chen, Stelios C.A. Thomopoulos, Stelios C.A. Thomopoulos, Ching-Fang Lin, Ching-Fang Lin, } "Feature estimation and object extraction using Markov random field modeling", Proc. SPIE 2485, Automatic Object Recognition V, (5 July 1995); doi: 10.1117/12.213103; https://doi.org/10.1117/12.213103
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