A Comparison of various Edge Detection Techniques used in Image Processing

In this paper the important problem is to understand the fundamental concepts of various filters and apply these filters in identifying a shark fish type which is taken as a case study. In this paper the edge detection techniques are taken for consideration. The software is implemented using MATLAB. The main two operators in image processing are Gradient and Laplacian operators. The case study deals with observation of Shark Fish Classification through Image Processing using the various filters which are mainly gradient based Roberts, Sobel and Prewitt edge detection operators, Laplacian based edge detector and Canny edge detector. The advantages and disadvantages of these filters are comprehensively dealt in this study.

[1]  Gérard G. Medioni,et al.  Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  W. B. Marks,et al.  Edge detection in images using Marr-Hildreth filtering techniques , 1988, Journal of Neuroscience Methods.

[4]  N. Kanopoulos,et al.  Design of an image edge detection filter using the Sobel operator , 1988 .

[5]  D. Mintz.  Robust consensus based edge detection , 1994 .

[6]  N. Senthilkumaran,et al.  Image Segmentation - A Survey of Soft Computing Approaches , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[7]  Muhammad Nadzir Marsono,et al.  A hardware architecture of Prewitt edge detection , 2010, 2010 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology.

[8]  Lei Yang,et al.  An improved Sobel algorithm based on median filter , 2010, 2010 2nd International Conference on Mechanical and Electronics Engineering.

[9]  Lei Yang,et al.  An improved Sobel edge detection , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[10]  Ivor W. Tsang,et al.  Laplacian Embedded Regression for Scalable Manifold Regularization , 2012, IEEE Transactions on Neural Networks and Learning Systems.