Abstract

Edge is the foremost feature of the image. Edges can be defined as boundary between regions in an image. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. Edge detection process reduces the amount of data and filters out useless information, while preserving the necessary structural properties in an image. In this paper, the main purpose is to study edge detection process based on different techniques.

References

• W. Frei and C. Chen, "Fast Boundary Detection: A Generalization and New Algorithm," IEEE Trans. Computers, vol. C-26, no. 10, pp. 988-998, Oct. 1977.
• J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 679-698, Nov. 1986
• Raman Maini and Dr. Himanshu Aggarwal “Study and Comparison of various Image Edge Detection Techniques” International Journal of Image Processing (IJIP), Vol3: Issue (1).
• S.Lakshmi and Dr.V.Sankaranarayanan “A study of Edge Detection Techniques for Segmentation Computing Approaches” IJCA Special Issue on Imaging and Biomedical Applications” CASCT, 2010. Edge Detection by Trucco,Chapter 4 and Jain ctal.,Chapter 5.
• Raman Maini and Dr. Himanshu Aggarwal “Study and Comparison of various Image Edge Detection Techniques” International Journal of Image Processing (IJIP), Vol3: Issue (1).
• W.Luo, Efficient Removal of Impulse Noise from Digital Images”, IEEE Transactions, 2006, 523527.
• P.Kamboj, V.Rani, Image Enhancement Using Hybrid Filtering Technique, IJSR, vol. 2(6), 2013, 214-220.
• Zolqemine Othman, habibollahharon, Mohammed Rafi, Abdul kadir, ―Comparison of canny and Sobel edge detection in mri images.
• M sudarsha*” p ganga Mohan and suryakanth v gangashetty ―Optimized edge detection algorithm for face recognition”.
• C.NagaRaju ,S.NagaMani, G.rakesh Prasad, S.Sunitha,"Morphological Edge Detection Algorithm based on Multi-Structure Elements of Different Directions",IJICT, Volume 1 No. 1, May 2011.
• K Sai Deepak JayanthiSivaswamy,"Automatic Assessment of Macular Edema from ColorRetinalImages",IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 31, NO. 3 MARCH 2012.
• HosseinGhayoumiZadeh, SiamakJanianpour, and JavadHaddadnia,"Recognition and Classification of Cancer cells by using Image Processing and LABVIEW", International Journal of Computer Theory and Engineering, Vol. 5, No. 1, February 2013.
• R. C. Gonzalez and R. E. Woods, Digital Image Processing. Upper Saddle River, NJ: Prentice-Hall, 2001, pp. 572-585
• W. K. Pratt, Digital Image Processing. New York, NY: Wiley-Interscience, 1991, pp. 491-556.
• 3J. Canny, “Finding Edges and Lines in Images,” Massachusetts Institute of Technology, Cambridge, Massachusetts, USA, Tech. Rep. no. 720, 1983
• B.Goossens.Q.LuongA.Pizurica.W Phillips, An improved non-local denoising algorithm, in: Local and Non-Local Approximation in Image Processing International Workshop, Proceeding,2008,pp.143-156.
• G.W. Wei, Discrete singular convolution for the solution of the Fokker–Planck equations, J. Chem. Phys. 110 (1999) 8930–8942.
• R.H.Chan, C.W. Ho,andM.Nikolova, “Salt-and-Pepper Noise Removal by Median-Type Noise Detectors and Detail-Preserving Regularization,” IEEE Transactions on Image Processing, vol. 14, No. 10, pp.1479-1485, 2005.
• Wang, Changhong, T. Chen, and Z. Qu., "A novel improved median filter for salt and pepper noise from highly corrupted images." In Systems and Control in Aeronautics and Astronautics (ISSCAA), vol. 3, pp. 718-722, 2010.
• Daniel L. Schmoldt, Pei Li and A. Lynn Abbott, “Machine vision using artificial neural networks with local 3D neighbourhoods”, Computers and Electronics in Agriculture, vol.16, 1997, pp.255-271.
• M. Zhao, A. Fu, and H. Yan, "A Technique of Three-Level Thresholding Based on Probability Partition a Fuzzy 3-Partition,"IEEE Trans. on Fuzzy Systems, vol.9, no.3, pp. 469 479, June 2001.