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M. Heath, S. Sarkar, T. Sanocki and K. W. Bowyer, “A Robust Visual Method for Assessing the Relative Performance of Edge Detection Algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 19, No. 12, 1997, pp. 1338-1359. http://dx.doi.org/10.1109/34.643893

has been cited by the following article:

• TITLE: Multi-Threshold Algorithm Based on Havrda and Charvat Entropy for Edge Detection in Satellite Grayscale Images

  AUTHORS: Mohamed A. El-Sayed, Hamida A. M. Sennari

  KEYWORDS: Multi-Threshold; Edge Detection; Measure Entropy; Havrda & Charvat’s Entropy

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.7 No.1, January 17, 2014

  ABSTRACT: Automatic edge detection of an image is considered a type of crucial information that can be extracted by applying detectors with different techniques. It is a main tool in pattern recognition, image segmentation, and scene analysis. This paper introduces an edge-detection algorithm, which generates multi-threshold values. It is based on non-Shannon measures such as Havrda & Charvat’s entropy, which is commonly used in gray level image analysis in many types of images such as satellite grayscale images. The proposed edge detection performance is compared to the previous classic methods, such as Roberts, Prewitt, and Sobel methods. Numerical results underline the robustness of the presented approach and different applications are shown.