Date of Award


Degree Name

Master of Science


Electrical and Computer Engineering

First Advisor

Dr. Frank L. Severance

Second Advisor

Dr. Hossein Mousavineshad

Third Advisor

Dr. John Kapenga

Access Setting

Masters Thesis-Open Access


One of the major problems in image processing is that of efficient edge detection. This is because the automated determination of an edge requires a subjective interpretation of just what the image author intended to be an edge. By training an edge determination algorithm to be sensitive to edges in a consistent manner, efficiency can be improved. This is the central idea of this thesis in which I define edge occurrences for the intensity (monochrome) images using fuzzy sets.

Edge characteristic functions are proposed for detecting edge pixels within a desired block of an (intensity and binary) image based on quadruple child windowing and binary edge patterns. I called these functions QCW-ECF. Fuzzy theory has been also applied to extend the QCW-ECF algorithm to the FQCW-ECF algorithm for edge detection within a block of a fuzzy intensity image. The (F)QCW-ECF results in a degree of edginess concerning the middle pixel of the processing block.

Cyclic coordinate algorithm is adopted to minimize the desired performance index of the Edge Detector System (EDS) by tuning the input/output membership functions of the Fuzzy Logic Controller (FLC). This is an optimization applied to the FQCW-ECF algorithm of edge detection. Finally, I have all the methods simulated against classical methods. The result are very promising.