Edge Detector Algorithms Ã¯Â¿Â½ PAGE \* MERGEFORMAT Ã¯Â¿Â½14Ã¯Â¿Â½
This paper investigates and presents mathematical and experimental foundation of two important edge detection algorithms. Sobel and Canny edge detectors have been compared with a brief comparison of their features.
Edge detection and edge enhancement algorithms are at the heart of image processing and are considered relatively beginner level topics however their implementation can be seen on a large scale and are found useful in various applications. Many different versions of edge detection algorithms have been developed by scientists and researchers so far each having its own pros and cons and effective use in relative applications.
Edge Detection is a vital process since edges in an image contain features set. By identifying features or edges of an image researchers store only important information and the rest is ignored that makes the image size smaller.
The paper starts with an overview of these two edge detection algorithms by presenting in brief each edge detector.
After the literature overview I have discussed as which algorithm tends to produce better results under what circumstances and after setting up the grounds I have conducted and presented experiments results to make a brief comparison. After presenting a brief comparison of both algorithms using algorithmic forms, I have drawn a clear and concise conclusion as which algorithm is given preference if various attributes are found within the image.
The ideal machine for computer vision or image processing is human perception of object itself. The way humans recognize the edges of an object based over color, contrast, depth and visual analysis is different from that of a computer automated algorithm. The most important candidate for identification of edge is difference in color intensity. Theoretically speaking in a computer automated program an abrupt change in color...