R. Manicka Chezian

Work place: Dr. Mahalingam Centre for Research and Development, NGM College, Pollachi, India

E-mail: chezian_r@yahoo.co.in


Research Interests: Data Structures and Algorithms, Data Compression, Data Mining, Distributed Computing, Computer systems and computational processes


Dr. R. Manicka chezian received his M.Sc Applied Science from PSG College of Technology, Coimbatore, India in 1987. He completed his M.S. degree in Software Systems from Birla Institute of Technology and Science, Pilani, Rajasthan, India and Ph.D degree in Computer Science from School of Computer Science and Engineering, Bharathiar University, Coimbatore. He has 25 years of Teaching experience and 17 years of Research Experience. He served as a Faculty of Maths and Computer Applications at P.S.G College of Technology, Coimbatore from 1987 to 1989. Presently, he is working as an Associate Professor of Computer Science in NGM College (Autonomous), Pollachi, India. He has published 75 papers in various International Journals and Conferences. He is a recipient of many awards like Desha Mithra Award and Best paper Award. He is a member of various Professional Bodies like Computer Society of India and Indian Science Congress Association. His research focuses on Network Databases, Data Mining, Distributed Computing, Data Compression, Mobile Computing and Real Time Systems.

Author Articles
Edge Detection Operators: Peak Signal to Noise Ratio Based Comparison

By D. Poobathy R. Manicka Chezian

DOI: https://doi.org/10.5815/ijigsp.2014.10.07, Pub. Date: 8 Sep. 2014

Edge detection is the vital task in digital image processing. It makes the image segmentation and pattern recognition more comfort. It also helps for object detection. There are many edge detectors available for pre-processing in computer vision. But, Canny, Sobel, Laplacian of Gaussian (LoG), Robert’s and Prewitt are most applied algorithms. This paper compares each of these operators by the manner of checking Peak signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) of resultant image. It evaluates the performance of each algorithm with Matlab and Java. The set of four universally standardized test images are used for the experimentation. The PSNR and MSE results are numeric values, based on that, performance of algorithms identified. The time required for each algorithm to detect edges is also documented. After the Experimentation, Canny operator found as the best among others in edge detection accuracy.

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