Image Processing: The Comparison of the Edge Detection Algorithms for Images in Matlab

Edge detection is the first step in image segmentation. Image Segmentation is the process of partitioning a digital image into multiple regions or sets of pixels. Edge detection is one of the most frequently used techniques in digital image processing. The goal of edge detection is to locate the pixels in the image that correspond to the edges of the objects seen in the image. Filtering, Enhancement and Detection are three steps of Edge detection. Images are often corrupted by random variations in intensity values, called noise. Some common types of noise are salt and pepper noise, impulse noise and Gaussian noise. However, there is a trade-off between edge strength and noise reduction. More filtering to reduce noise results in a loss of edge strength. In order to facilitate the detection of edges, it is essential to determine changes in intensity in the neighborhood of a point. Enhancement emphasizes pixels where there is a significant change in local intensity values and is usually performed by computing the gradient magnitude. Many points in an image have a nonzero value for the gradient, and not all of these points are edges for a particular application. Therefore, some method should be used to determine which points are edge points. Four most frequently used edge detection methods are used for comparison. These are: Roberts Edge Detection, Sobel Edge Detection, Prewitt Edge Detection and Canny Edge Detection. One the other method in edge detection is spatial filtering. This Paper represent a special mask for spatial filtering and compare throughput the standard edge detection algorithms (Sobel, Canny, Prewit & Roberts) with the spatial filtering.