Comparison of methods of edge detection of a metal drop image

Edge is the basic characteristic of an image, edge detection plays an important role in computer vision and image analysis. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. The purpose of edge detection is to mark the points in a digital image at which the luminous intensity changes sharply. Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. There are many ways to perform the edge detection. However, they may be grouped into two categories, that are edge detection techniques of Gradient-based and Laplacian based Edge Detection. The gradient method detects the edges by looking for the maximum and minimum in the first derivative of the image. The Laplacian method searches for the zero crossings in the second derivative of the image to find edges. In this paper the analysis of the results of Edge Detection by other scientists is presented. In this paper we have evaluated various Edge Detection Operators – they are Sobel, Robert, Prewitt, LOG and Canny – used for a metal drop images. The Robert, Prewitt, Sobel methods showed the best result