Evaluation of Various Edge Detection Algorithms ? Review

Edge detection is the most common preprocessing step in many image processing algorithms such as image enhancement, image segmentation, tracking and image/video coding. Edge detection is the name for a set of mathematical methods that aims at identifying points in a digital image at which the image brightness changes sharply and it has discontinuities. The points in which image brightness changes sharply are typically organized into a set of curved line segments termed as edges. The step detection is the problem of finding discontinuities in 1D signals and change detection is the problem of finding signal discontinuities over time. Edge detection is a fundamental tool particularly in the areas of feature detection and feature extraction. There are different algorithm based edge detectors like Canny, Sobel, Iverson and they depend on the input parameters. The purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world. In an image formation model, discontinuities in image brightness are likely to correspond to four assumptions: discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. Mamdani method is widely accepted for capturing expert knowledge. It allows us to describe the expertise in more intuitive, more human-like manner. This article discusses the recent development of edge detection algorithms available in image processing fields.