Markov Random Field Applications in Image Analysis

Image analysis, or computer vision, deals with machine processing of visual (or pictorial) information. Visual sensory data are usually obtained in the form of two-dimensional images of the physical world and consists of measurements which are dependent on factors such as the imaging geometry, illumination, and structures present in the world. The goal of any vision system is to recognize familiar structures in the system’s environment, and obtain concise symbolic descriptions of unfamiliar structures, using visual sensory data. The task of a vision system, then, can be described as a data reduction task which extracts meaningful descriptions from huge amounts of visual data. The description obtained from the visual data should remain invariant for certain changes in the physical world [3]. In many computer vision applications, invariance against the following factors is required [3]: (i) sensor noise; (ii) optical distortion; (iii) viewpoint; (iv) perspective distortion; and (v) variations in photometric conditions.

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