Maximization Of Contour Edge Detection Using Adaptive Thresholding


A new adaptive thresholding technique is presented that maximizes the contour edge information within an image. Early work by Attneave suggested that visual information in images is concentrated at the contours.1 The information content of curves is easily illustrated with Attneave's famous "Cheshire Cat", example. He showed that the information associated with a contour is not uniformly distributed along a curve, but concentrates at certain points of extrema. He further concluded that the information associated with these points and their nearby neighbors is essential for image perception. Resnikoff has suggested a measurement of information gain in terms of direction.2 This measurement determines information gained from a measure of an angle direction along image contours relative to other measures of information gain for other positions along the curve. Hence, one form of information measure is the angular entropy of contours within an image. Our adaptive thresholding algorithm begins by varying the threshold value between a minimum and a maximum threshold value and then computing the total contour entropy over the entire binarized edge image. Next, the threshold value that yields the highest contour entropy is selected as the optimum threshold value. It is at this threshold value that the binarized image contains the greatest amount of image features.

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Proceedings of SPIE - The International Society for Optical Engineering



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Article; Proceedings Paper



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85075833885 (Scopus)

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