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A thresholding technique using relative entropy is proposed for vapor cloud detection. The idea is to cast a detection problem as a thresholding problem where the relative entropy is chosen to be the detection criterion and the null and alternative hypotheses correspond to background and objects respectively. Since the information content in an image can be characterized by its entropy, the original image and the thresholded bilevel image can be viewed as two sources. As a result, the relative entropy becomes a natural measure to describe the mismatch between these two images. The smaller the relative entropy, the better the matching between the two images. In this paper, we interpret detection problems as image thresholding problems, where the null hypothesis corresponds to noise only and the alternative hypothesis represents presence of target. Three methods based on relative entropy are presented for chemical vapor cloud detection. The experimental results show that the suggested relative entropy-based methods can detect a vapor cloud very effectively. The performance is also compared against two recently developed entropic thresholding techniques, the local entropy and joint entropy proposed by S.R. Pal and S.K. Pal and shows that the relative entropy-based method outperform Pal and Pal's methods.
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