Edge Detection in Digital Images Using Fuzzy Logic Technique

The fuzzy technique is an operator introduced in order to simulate at a mathematical level the compensatory behavior in process of decision making or subjective evaluation. The following paper introduces such operators on hand of computer vision application. In this paper a novel method based on fuzzy logic reasoning strategy is proposed for edge detection in digital images without determining the threshold value. The proposed approach begins by segmenting the images into regions using floating 3x3 binary matrix. The edge pixels are mapped to a range of values distinct from each other. The robustness of the proposed method results for different captured images are compared to those obtained with the linear Sobel operator. It is gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. In the same time the corners get sharper and can be defined easily.

Authors:

• Abdallah A. Alshennawy
• Ayman A. Aly

Keywords:

• Fuzzy logic
• Edge detection
• Image processing
• computer vision
• Mechanical parts
• Measurement.

References:

[1] I.M. Elewa, H.H Soliman and A.A. Alshennawy. "Computer vision
Methodology for measurement and Inspection: Metrology in Production
area ". Mansoura Eng. First conf. Faculty of Eng. Mansoura Univ.,
March 28-30,1995,Pp. 473-444.
[2] A. A. Alshennawy, "Measurement and Inspection of Three Dimensional
Objects Using Computer Vision System", Pd.D thesis, Mansoura
University, Egypt, 2003.
[3] H. D. Hofmann, "Application of Intelligent Measurements with Metrical
Image Processing for Quality Control", presented at the 5th International
Conference, PEDAC' 92, Alexandria, EGYPT, December 1992.
[4] R.T. Chin., C.A. Harlow, "Automated Visual Inspection: A survey ",
IEEE Trans. Pattern Anal. Machine Intell., Vol. PAMI-4, pp. 557-573,
November 1982.
[5] P. Rummel, "GSS - A Fast, Model-Based Gray-Scale Sensor System for
Workpiece Recognition", Proceed. of 8th International Conf. on Pattern
Recognition, pp. 18-21, Paris, France, Oct. 27-31, 1986.
[6] B. G. Batchelor, D. A. Hill and D. C. Hodgson (Eds.), "Automated
Visual Inspection", IFS Publications Ltd., UK, 1985.
[7] A. M. Darwish and A. K. Jain, "A Rule Based Approach for Visual
Pattern Inspection ", IEEE Trans. on Pattern Analysis and Machine
Intelligence, Vol. 10, No. 1, pp. 56-68, January 1988.
[8] G. Maitre, H. Hugli, F. Tieche and J. P. Amann, "Range Image
Segmentation Based on Function Approximation", published at
ISPRS90, Zurich, Sept. 1990.
[9] Md. Shoiab Bhuiyan, Yuiji Iwahori, and Akira Iwata., Optimal edge
detection under difficult imaging conditions. Technical report,
Educational Center for Information Processing and Dept. of Electrical
and Computer Engineering, Nagoya Institute of Technology, Showa,
Nagoya, 466-8555, JAPAN.
[10] Srinivasan,M. V., Chahl, J. S.,Weber, K., Venkatesh, S., Nagle, M. G.,
and Zhang, S.W.: Robot navigation inspired by principles of insect
vision, Robotics Autonom. Systems 26(2/3) (1999), 203-216.
[11] Suh, I. H. and Kim, T.W.: Fuzzy membership function-based neural
networks with applications to the visual servoing of robot manipulators,
IEEE Trans. Fuzzy Systems 2(3) (1994), 203-220.
[12] Md. Shoaib Bhuiyan, Hiroshi Matsuo, Akira Iwata, Hideo Fujimoto, and
Makoto Sato. An improved neural network based edge detection
method. Technical report, Dept. of Electrical and Computer Engineering
and Dept. of Mechanical Engineering, Nagoya Institute of Technology,
Nagoya, JAPAN 466.
[13] I. Fourmousis, U. Burgin, M. Tonetti, and N.P. Lang, "Digital image
processing I Evaluation of gray level correction methods in vitro", Clin.
Oral Impl. Res., pp.1-11, Sept. 1993.
[14] H.G. Grondahl, K. Grondahl, and R.L. Webber, "A digital subtraction
technique for dental radiography", Oral Surg., vol.55, number 1, pp.96-
102, Jan. 1983.
[15] P.F. van der Stelt, W.J. van der Linden, W.G.M. Geraets, C.L. Alons,
"Digitized pattern recognition in the diagnosis of periodontal bone
defects", Journal of Clinical Periodontology, vol. 12, p.822-827, 1985.
[16] Santiago Aja, C. Alborola, J. Ruiz, "Fuzzy anisotropic diffusion for
speckle filtering", IEEE Intl. Conf. on Acoustics, Speech, and Signal
Processing, Vol. 2 , 2001, pp. 1262-1264.
[17] G.I. Sanchez-Ortiz, A. Noble,"Fuzzy clustering driven anisotropic
diffusion: enhancement and segmentation of cardiac MR images",
Nuclear Science Symposium, Vol. 3 , 1998, pp. 1873 -1874.
[18] R.L. Webber, U.E. Ruttimann, and R.A.J. Groenhuis, "Computer
correction of projective distortions in Dental Radiographs", J. Den. Res.,
vol. 63, No. 8, pp.1032-1036, August 1984.
[19] Song Wang, Feng Ge, Tiecheng Liu, "Evaluating Edge Detection
Through Boundary Detection", Department of Computer Science and
Engineering, University of South Carolina, Columbia, SC 29208, USA,
2006.
[20] Ivan Christov, " Multiscale Image Edge Detection",1.130/18.327, Spring
2004, Final Project, May 12, 2004.
[21] Nor Ashidi Mat Isa, " Automated Edge Detection Technique for Pap
Smear Images Using Moving K-Means Clustering and Modified Seed
Based Region Growing Algorithm", International Journal of The
Computer, the Internet and Management Vol. 13.No.3 (September-
December, 2005) pp 45-59.
[22] C.C. Leung, F.H.Y. Chan, K.Y. Zee, P.C.K. Kwok, "Compensation of
bending errors in Intra-oral Radiographs using Block-by-Block Image
Scaling", IEEE Trans. On Biomedical Engineering. (In manuscript).
[23] C.C. Leung, P.C.K. Kwok, K.Y. Zee, F.H.Y. Chan, and S.T.F. Lo,
"Minimizing the bending error in Intra-oral Radiographs using Point-by-
Point Interpolation with image scaling", Proceedings of the EMBEC'99,
Part II, pp.1050-1051, Nov. 1999. Vienna.
[24] W.F. Chen, X.Q. Lu, J.J. Chen, and G.X. Wu, "A new algorithm of edge
detection for color image: Generalized fuzzy operator", Science in China
(Series A), Vol.38, No.3, pp.468-473, 1998.
[25] C.C. Leung, F.H.Y. Chan, W.F. Chen, P.C.K. Kwok and K.Y. Lam,
"Thyroid Cancer Cells Boundary Location by a Fuzzy Edge Detection
Method" ICPR-2000, Sept. 2000.
[26] Kuo, R. J.: A robotic die polishing system through fuzzy neural
networks, Comput. Industry 32(3) (1997), 273-280.
[27] Tizhoosh H.R., "Fast fuzzy edge detection", Proceedings of Fuzzy
Information Processing Society, 2002, pp. 239-242.
[28] Lin, C. T. and Lee, S. G.: Reinforcement structure/parameter learning
for neural network based fuzzy logic systems, IEEE Trans. Fuzzy
Systems 2(1) (1994), 46-63.
[29] Ayman A. Aly, H. Ohuchi and A. Abo-Ismail, "Fuzzy Model Reference
Learning Control of 6-Axis Motion Base Manipulator", 7th IEEE
International Conference on Intelligent Engineering Systems, Luxer,
March, 2003.