Title

Maximization Of Contour Edge Detection Using Adaptive Thresholding

Abstract

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.

Publication Date

9-3-1993

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

1955

Number of Pages

400-407

Document Type

Article; Proceedings Paper

Identifier

scopus

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.154995

Socpus ID

85075833885 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85075833885

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