Analysis: A Computational Approach To Edge Detection

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The Canny edge detection algorithm is commonly known as the optimal edge detector. During his research work, Canny's main intentions were to enhance the edge detectors which were already out at that time. Canny was successful in his objective and published a paper entitled "A Computational Approach to Edge Detection" in which he mentions a list of criteria which could improve current methods of edge detection. According to him, low error rate was one of the important criteria. Secondly, the edges in the image must not be missed and there must be no response to non-edges. Thirdly, the edge points must be well localized that is the distance between the edge pixels found by the detector and the actual edge must be minimum. And lastly, only one response Gaussian filter is exclusively used for this purpose as the mask is simple. The standard convolution method is performed once the mask is calculated. Since the convolution mask is usually much smaller than the actual image, the mask slides over the image , manipulating the pixels in the image. The large width Gaussian masks are not preferred as detector's sensitivity to noise is low and moreover, the localization error in the detected edges also increases with increase in Gaussian mask width. Step 2:- After the initial pre processing steps of smoothening and removal of noise, the edge strength is calculated by taking the gradient of the image. For the purpose of edge detection in an image, the Sobel operator first performs a 2-D spatial gradient measurement with the help of convolution masks. The convolution masks used is of the size 3X3, where one is used to calculate the horizontal gradient(Gx) while the other is used to calculate the vertical gradient(Gy). Then, the approximate absolute edge strength can be calculated at each point. The masks used for the convolution process is as shown

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