Edge detection is a basic and important
subject in computer vision and image processing. An edge
detector is defined as a mathematical operator of small
spatial extent that responds in some way to these
discontinuities, usually classifying every image pixel as either
belonging to an edge or not. Many researchers have been
spent attempting to develop effective edge detection
algorithms. Despite this extensive research, the task of
finding the edges that correspond to true physical
boundaries remains a difficult problem.Edge detection
algorithms based on the application of human knowledge
show their flexibility and suggest that the use of human
knowledge is a reasonable alternative. In this paper we
propose a fuzzy inference system with two inputs: gradient
and wavelet details. First input is calculated by Sobel
operator and the second is calculated by wavelet transform
of input image and then reconstruction of image only with
details subimages by inverse wavelet transform. There are
many fuzzy edge detection methods, but none of them utilize
wavelet transform as it is used in this paper. For evaluating
our method, we detect edges of images with different
brightness characteristics and compare results with canny
edge detector. The results show the high performance of our
method in finding true edges.
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