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Edge means finding such coordinates of image where there is abrupt change. There are different methods to detect the edge such as Sobel, Canny, Prewitt, Roberts and fuzzy logic. I am referring here the block diagram of edge detection using fuzzy logic.
Let us discuss the block diagram of Figure 1. Image is converted to grayscale image so that one can focus on only one channel. RGB contains three channel whereas grayscale image contains only one channel. Edge is defined as those pixels which shows large change in the intensity value and hence it is appropriate to calculate the gradient because it denotes those value where the maximum change takes place and points in that direction where the maximum change take place.
To achieve such the image is slides over the kernel or filter which is termed as Convolution operation which gives image gradient in x axis and y axis respectively. Now it is time to calculate the magnitude which can be seen in the above image. Design a fuzzy system which is feeded with image gradient in x axis and y axis respectively. You can design the fuzzy system using triangular or Gaussian membership function as per your requirement. Apply the rule how the fuzzy system will detect the edge and at the end you will get the edged image. Along with using the fuzzy logic, one can detect the edge using canny, sobel, roberts and prewitt which are very famous. One can also use optimization method or genetic algorithm to achieve the same.
Abrupt Edge method for Edge detection: This method is implementation of logic behind finding the edge of image. Central Pixel is chosen and difference between such pixel and surrounding pixels are analyzed and threshold is set. If the difference is greater than threshold, such pixel is edge otherwise it is not. Please refer the following figure to understand the concept in more detail.
Please see the Graphical user interface for such system which I am showing in the next image.
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