Flow of process edge detection A. Edge Detection A key factor in extracting characteristic of an image is the ability to detect the presence of edges of the object in the image. Edge detection become the first step to cover information inside the image, edge characterize object boundaries and because it is useful for the edge segmentation and identification in the image. Edge detection is the goal to improve the appearance of lines boundary of an area or object in the image. B. Canny Operator One of the edge detection algorithms is edge detection using Canny method. Canny edge detection was found by Marr and Hildreth were examined modeling of human visual perception. There are several criteria for optimum edge detection that can be met by Canny algorithm:  Properly detect (detection criteria) Ability to lay and mark all the edges that are applicable to the selection of the parameters of convolution is performed. It also gives a very high flexibility in terms of determining the level of detection of the desired edge thickness.  Localize properly (localization criteria) Produced by Canny possible minimum distance between the edges detected by the edge of the original.  Clear response (response criteria) There is only one response for each edge. So easy to detect and do not cause confusion in the subsequent image processing. Canny edge detection parameter selection greatly affects the outcome of the resulting edge. Some of these parameters include:  Standard deviation value of the Gaussian  Threshold Canny operator is the optimum-approaching operator of the product of SNR and the location. Canny algorithm smoothers image by Gaussian filter, calculates the magnitude and direction of gray level gradient, has the non-maxima suppression on gradient magnitude, and detect and connect the