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A Fuzzy Set Approach for Edge Detection
Pushpajit A.Khaire, Dr. Nileshsingh V. Thakur
Pages - 403 - 412     |    Revised - 15-11-2012     |    Published - 31-12-2012
Volume - 6   Issue - 6    |    Publication Date - December 2012  Table of Contents
Edge Detection, Fuzzy Set, BSD (Berkeley Segmentation Database), Ground Truth, PSNR
Image segmentation is one of the most studied problems in image analysis, computer vision, pattern recognition etc. Edge detection is a discontinuity based approach used for image segmentation. In this paper, an edge detection using fuzzy set is proposed, where an image is considered as a fuzzy set and pixels are taken as elements of fuzzy set. The fuzzy approach converts the color image to a partially segmented image; finally an edge detector is convolved over the partially segmented image to obtain an edged image. The approach is implemented using MATLAB 7.11. (R2010b). For qualitative and quantitative comparison, BSD (Berkeley Segmentation Database) images are used for experimentation. Performance parameters used are PSNR (dB) and Performance ratio (PR) of true to false edges. It has been shown that the proposed approach performs better than Canny’s edge detection algorithm under almost all scenarios. The proposed approach reduces false edge detection and double edges.
CITED BY (13)  
1 Patil, P. R. A review on edge detection methodologies.
2 Anuradha, S. G., Karibasappa, K., & Reddy, B. E. (2015). Morphological Change Detection System for Real Time Traffic Analysis. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(8), 143-150.
3 Kiani, A., & Ebadi, H. (2015). Development of a New Method for Edge Detection from High-Resolution Aerial/Satellite Images, with Emphasis on Threshold Optimization and Using Imperialist Competitive Algorithm. Journal of Geomatics Science and Technology, 4(4), 67-82.
4 Nisha, R. M., & Sharma, L. Comparative Analysis of Canny and Prewitt Edge Detection Techniques used in Image Processing.
5 Charaâ, S., & Ellouze, N. (2015). Discriminating color space selection for edgDiscriminating edge detection using multiscale product wavelet transform. International Journal of Computer Science Issues (IJCSI), 12(1), 11.
6 Parihar, V. R., & Thakur, N. V. (2014). Graph Theory Based Approach For Image Segmentation Using Wavelet Transform. International Journal of Image Processing (IJIP), 8(5), 255.
7 Charaa, S., & Ellouze, N. (2014, April). Multiscale Product Edge Detection in Different Colour Spaces. In Information Technology: New Generations (ITNG), 2014 11th International Conference on (pp. 660-664). IEEE.
8 Abo-Zahhad, M., Gharieb, R. R., Ahmed, S. M., & Donkol, A. A. E. B. (2014). Edge Detection with a Preprocessing Approach. Journal of Signal and Information Processing, 5(04), 123.
9 Abo-Zahhad, M., Gharieb, R. R., Ahmed, S. M., & Donkol, A. A. (2014). Enhancement of Gabor Directional Wavelet Edge Detection Method. IJEIR, 3(6), 839-849.
10 Poobathy, D., & Chezian, R. M. (2014). Edge Detection Operators: Peak Signal to Noise Ratio Based Comparison. International Journal of Image, Graphics and Signal Processing (IJIGSP), 6(10), 55.
11 Joshi, N. S., & Choubey, N. S. Application of Soft Computing Approach for Edge Detection.
12 Joshi, N. S., Choubey, N. S., & Dwivedi, R. (2013). Overview of Edge Detection Techniques. Open Journal of Computer Science and Information Technology, 1(1), 20-32.
13 Anisha, S. N., & Krishna, G. R. Nav view search.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
Abdallah A. Alshennawy, and Ayman A. Aly, “Edge Detection in Digital Images Using Fuzzy Logic Technique”, World Academy of Science, Engineering and Technology, 2009, pp-178-186.
Aborisade, D.O, “Fuzzy Logic Based Digital Image Edge Detection”, Global Journal of Computer Science and Technology, Volume 10, November 2010, pp- 78-83.
Adam Hoover and et al., “An Experimental Comparison of Range Image Segmentation Algorithms”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18 no.7 July 1996.pp- 673-689.
Bhoyar and Kakde, “Color Image Segmentation using Fast Fuzzy C-Means Algorithm”,Electronic letters on (CVIA) 2010, pp-18-31.
Chandra Sekhar Panda and Srikanta Patnaik, “Filtering Corrupted Image and Edge Detection in Restored Grayscale Image Using Derivative Filters”, International Journal of Image Processing (IJIP), Volume 3, 2010, pp- 105-119.
D. Martin, C. Fowlkes, D. Tal and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics”, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV’01), 2001, pp- 416–425.
Evans, A. N. and Lin, X. U., “A Morphological Gradient Approach to Color Edge Detection”,IEEE Transactions on Image Processing, 15 (6), pp. 1454-1463, 2006.
Francisco J. Estrada and Allan D. Jepson, “Benchmarking Image Segmentation Algorithms”, International Journal of Computer Vision. Vol. 85, no. 2, Nov 2009, pp. 167-181.
George J.Klir and Bo Yuan, “Fuzzy sets and Fuzzy logic: Theory and Applications”, Prentice Hall, 1995.
J. Canny, “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence. 8 (6), pp- 679-687, 1986.
J. Patel, J. Patwardhan, K Sankhe and R Kumbhare, “Fuzzy Inference based Edge Detection System using Sobel and Laplacian of Gaussian Operators”, ICWET’11, ACM 978-1-4503-0449-8, February 25–26, 2011, pp- 694-697.
K. Bowyer, C. Kranenburg, and S. Dougherty, “Edge Detector Evaluation Using Empirical ROC Curves,” Computer Vision and Image Understanding, vol. 84, no. 1, Oct. 2001, pp- 77-103
Kaustubha Mendhurwar, Shivaji Patil, Harsh Sundani, Priyanka Aggarwal, and Vijay Devabhaktuni, “Edge-Detection in Noisy Images Using Independent Component Analysis”,ISRN Signal Processing, 9 pages, February 2011.
M. Heath, S. Sarkar, T. Sanocki, and K.W. Bowyer, “Comparison of Edge Detectors: A Methodology and Initial Study”, Computer Vision and Image Understanding, vol. 69, no. 1,Jan. 1998, pp- 38-54.
Mahdi Setayesh, Mengjie Zhang, and Mark Johnston, “Detection of Continuous, Smooth and Thin Edges in Noisy Images Using Constrained Particle Swarm Optimization”, ACM 978-1-4503-0557-0, GECCO’11, Dublin, Ireland, July 12–16, 2011, pp- 45-52.
N. Pal and S. Pal, “A Review on Image Segmentation Techniques”, Pattern Recognition,Vol. 26, no. 9, 1993, pp- 1,277-1, 294.
O. J. Tobias and R. Seara, “Image segmentation by histogram thresholding using fuzzy sets”, IEEE Transaction on Image Processing., Vol. 11, 2002, pp-1457-1465.
R. C. Gonzalez and R. E. Woods. “Digital Image Processing”, Addison Wesley, 2nd edition,1992.
R. Unnikrishnan, C. Pantofaru, and M. Hebert, “Towards objective evaluation of image segmentation algorithms”, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (6), (2007), pp-929–944.
Raman Maini and Himanshu Aggarwal, “Study and Comparison of Various Image Edge Detection Techniques”, International Journal of Image Processing (IJIP), Volume (3), 2010,pp-1-12.
Raman Maini, J.S.Sohal, “Performance Evaluation of Prewitt Edge Detector for Noisy Images”, GVIP Journal, Volume 6, December, 2006.
S. Konishi, A. Yuille, J. Coughlan and S.C. Zhu, “Statistical Edge Detection: Learning and Evaluating Edge Cues”, IEEE Transactions on Pattern Analysis and Machine Intelligence,Jan, 2003.
Song Wang, Feng Ge, and Tiecheng Liu, “Evaluating Edge Detection through Boundary Detection”, EURASIP Journal on Applied Signal Processing, Article ID 76278, June 2006 Pages 1–15.
Wenshuo Gao and et al., “An Improved Sobel Edge Detection”, 978-1-4244-5540-9 IEEE,ICICT 2010.
Mr. Pushpajit A.Khaire
RCOEM - India
Dr. Dr. Nileshsingh V. Thakur
- India