Markov random field modeling in computer vision
Markov random field modeling in computer vision
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This paper proposes an efficient method to extract targets from an infrared image. First, the regions of interests (ROIs) which contain the entire targets and a little background region are detected based on the variance weighted information entropy feature. Second, the infrared image is modeled by Gaussian Markov random field, and the ROIs are used as the target regions while the remaining region as the background to perform the initial segmentation. Finally, by searching solution space within the ROIs, the targets are accurately extracted by energy minimization using the iterated condition mode. Because the iterated segmentation results are updated within the ROIs only, this coarse-to-fine extraction method can greatly accelerate the convergence speed and efficiently reduce the interference of background noise. Experimental results of the real infrared images demonstrate that the proposed method can extract single and multiple infrared objects accurately and rapidly.