A Comprehensive Study of Edge Detection for Image Processing Applications

In this paper, a comprehensive study of edge detection methods for image processing applications is carried out to analyze the various edge detectors and the latest trends in edge detection. An Edge in image processing can be described as discontinuities in intensity from one pixel to another. Edge detection is one of the most useful image enhancement techniques to improve the quality of the image analysis process. The principal objective of the edge detection is to identify and classify the discontinuities in an image. The edge detection in image processing considerably lessen the quantity of data (pixel) to represent an image and also filters out the futile information, while keeping the essential structural assets of an image. However, it is very difficult to perform edge detection in noisy images since it is uphill task to distinguish both the edges and noise in the image because both of them having high frequency components. In the past few decades, numbers of methods have been proposed for the detection of edges in color and intensity images. However, the edge detection is application (problem) oriented i.e., we can't apply a same algorithm for all types of images (applications). In this paper, an elaborative comparison of various edge detection methods for various image processing applications is performed.