Moving object segmentation using Markov Random Field 


Vol. 27,  No. 3, pp. 221-230, Mar.  2002


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  Abstract

This paper presents a new moving object segmentation algorithm using markov random field. The algorithm is based on signal detection theory. That is to say, motion of moving object is decided by binary decision rule, and false decision is corrected by markov random field model. The procedure toward complete segmentation consists of two steps: motion detection and object segmentation. First, motion detection decides the presence of motion on velocity vector by binary decision rule. And velocity vector is generated by optical flow. Second, object segmentation cancels noise by Bayes rule. Experimental results demonstrate the efficiency of the presented method.

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  Cite this article

[IEEE Style]

C. Jung and J. Kim, "Moving object segmentation using Markov Random Field," The Journal of Korean Institute of Communications and Information Sciences, vol. 27, no. 3, pp. 221-230, 2002. DOI: .

[ACM Style]

Cheolkon Jung and Joongkyu Kim. 2002. Moving object segmentation using Markov Random Field. The Journal of Korean Institute of Communications and Information Sciences, 27, 3, (2002), 221-230. DOI: .

[KICS Style]

Cheolkon Jung and Joongkyu Kim, "Moving object segmentation using Markov Random Field," The Journal of Korean Institute of Communications and Information Sciences, vol. 27, no. 3, pp. 221-230, 3. 2002.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880