An Adaptive Approach to Improve Canny Method for Edge Detection

Edge detection is the most important stages in digital image processing for all fields of applications. Out of many approaches for edge detection such as Sobel, Prewitt, Canny, Laplacian of Gaussian method and Robert, Canny approach is the best. Canny edge detector is widely used in computer vision and medical imaging to locate sharp intensity changes and to find object boundaries in an image. The Canny edge detection algorithm is most widely used edge detection algorithm because of its advantages, whereby it classifies a pixel as an edge if the gradient magnitude of the pixel is larger than those of pixels at both its sides in the direction of maximum intensity change. This paper presents an adaptive approach for improving Canny algorithm which uses an adaptive Gaussian filter to smoothen the noise and edges differently and employing variable sigma and threshold values for different parts of the image.