Development of data driven adaptive edge detectors for image processing

Abstract

In most vision processing activities, the early stage involves identifying the features in an image that provide cues to structure and properties of the object in the scene. Most common features in an image or in a scene arel edges. Edges arel significant local changes in intensity within anlimage. Most important goal of edge detection is to produce a line drawing from anlimage representing the scene. The significant features of an image such as line, curve and corners can be extracted from edges. During the stage of discovering and exploring the information from an image of that scene, edge detection is the most important and early-stage activity and as such it is prominent active area in image processing. Most popular edge detection algorithm such as Robert, Sobel, Canny, Prewitt and Laplacian of Gaussian (LoG), etc. are currently in use. This paper emphasis on an experimental study of limitations of conventional edge detectors and to devise a novel approach to resolve the conflicting issues i.e., limitations of these edge detectors in adaptive space utilizing novel methods such as Bi-dimensional Empirical Mode Decomposition (BEMD), Image Empirical Mode Decomposition (IEMD), Complete Ensemble Empirical Mode Decomposition (CEEMD) and Multivariate Decomposition techniques. Further, to study the performance of these modified edge detectors on the images of complex scenes which are of societal and agricultural importance.