Analysis of Edge Detection Methods in Image Processing

Image processing is a very effective method for interpreting the contents of an image and process them for various uses. The main objective of image processing is to find the meaningful and valid information from the contents of the image. Digital image processing has achieved a lot of attention from the researchers in this era. The interpretation of digital images through the various image processing techniques and algorithms has helped a lot of researchers for analysing images to collect valuable information in various fields. The image processing algorithms allows us to use simple and effective algorithms which will give lots of information which is not possible by other analog means. Image segmentation is a method in which we divide the images into smaller regions of similar contrast, is an essential process in the handling of a digital images. It allows us to read the image more efficiently and analyse each part more carefully. The best technique for image segmentation is edge detection. Detecting the edges which reside in the image helps for segmenting an image properly. Edge can be termed as fundamental feature present in an image. A digital image is a set of pixels to represent each point in the image which display a certain contrast or shade. An image contains different regions with different contrast. The edge detection process detects such variations in contrast. This process allows us to filter image by removing unnecessary information but keeping significant information. Edge detection greatly reduces the size of the image while preserving its features and structure. The edge detection method identifies different patterns in images and give proper size and shape of different objects and structures in an image. In this paper, we will discuss standard edge detection algorithms which are used for image processing. We will be discussing about the role of Robert, Prewitt andSobel operator, Canny operator andLaplacian of Gaussian detector etc.