Image Thresholding by Maximizing the Index of Nonfuzziness of the 2-D Grayscale Histogram
Image segmentation plays an important role in various image processing applications including robot vision and document image analysis and understanding. In contrast to classical set theory, fuzzy set theory, which takes into account the uncertainty intrinsic to various images, has found great success in the area of image thresholding. In this paper, an image thresholding approach based on the index of nonfuzziness maximization of the 2-D grayscale histogram is proposed. The threshold Vector (T, S), where T is a threshold for pixel intensity and S is another threshold for the local average of pixels, is obtained by an exhaustive searching algorithm. In this approach, the difference between these two components (T and S) is guaranteed to be within a relatively small range, which leads to reasonable results from the viewpoint of human vision perception. This cannot be achieved in certain entropy-based methods. Experimental results have shown that our proposed approach not only performs well and effectively but also is more robust when applied to noisy images.