A New Window Selection for Local Image Thresholding under Uneven Illuminations
نویسندگان
چکیده
Image thresholding is one of the most powerful techniques for image segmentation, but it is not always satisfactory in applications under uneven illuminations. Adaptive image thresholding is used to find the optimal window for solving the illumination problem. In this paper, a novel window selection method for adaptive local thresholding is proposed. Based on simulated annealing, the proposed algorithm searches the optimal window among partitioned subimages on the quadtree data structure from bottom up. It can be applied to other existing methods to improve the performance of image thresholding. Experimental results show the efficiency of the proposed method.
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