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Top2. Literature Review
In an image, the distance between an object and background or between overlapped objects is called edge. Assuming that each image has uniform light intensity, the light intensity of the adjacent objects have different amounts and any variation in light intensity is considered linear filters results in ease of edge detection and decrease in calculations in order to get desired results.
In the reference Pithadiya (2009)˛ edge extraction concepts and edge detection processes in canny algorithm as well as morphological algorithm have been reviewed, the practical results of forenamed algorithms for clarity of edges in aerial photography of different areas of the city have been investigated.
Ke et al. (2008) presented the cloud model is used for analyzing the edge information of the images in evolutionary modes of cell automata, Also it is used for finding the relationship between the adjacent pixels.
Abin et al. in 2009 proposed a new method which was presented for segmenting the color images by using soft and hard segmenting processes. The process of soft segmentation of images and provides the threshold mode till it reaches the final segmentation and in hard segmentation, CLA analysis are done on input image and pixels of each part of the image.
The algorithms presented to edge detection (Michael, Sudeep, Thomas, & Kevin, 1997; Abin, Fotouhi, & Kasaei, 2009; Abin, Fotouhi, & Kasaei, 2008). Are classic methods of edge detection such as Robert, Sobel which are calculates the gradient local maximum in the domain of location. If pixels are placed on the picture borders, their adjacency will be on gray-levels. For detecting edges on crossover areas in Laplace conversion domain, the crossing points of second derivative of image function is considered as edges.