Image Edge Detection Using Fuzzy Logic ―Compatibility Mode‖
Abstract
Digital image processing is a subset of the electronic
domain wherein the image is converted to an array of small integers,called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Edges characterize boundaries and Edge detection is one of the most difficult tasks in image processing hence it is a problem of fundamental importance in image processing. Edges in
images are areas with strong intensity contrasts and a jump in
intensity from one pixel to the next can create major variation in the picture quality. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Fuzzy logic represents a good mathematical framework to deal with uncertainty of information. Fuzzy image processing is the collection of all approaches that understand, represent and process the
images, their segments and features as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. This research problem deals with Fuzzy inference system (FIS) which represents greater robustness to contrast and lighting variations. Further tuning of the weights associated to the fuzzy inference rules is still necessary to reduce
even more inclusion in the output image of pixels not belonging toedges.
Keywords
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Dailey D. J., Cathey F. W. and Pumrin S. 2000. An Algorithm to
Estimate Mean Traffic Speed Using Uncalibrated Cameras. In
proceedings of IEEE Transactions on intelligent transport systems, Vol.
Desai U. Y., Mizuki M. M., Masaki I., and Berthold K.P. 1996. Edge
and Mean Based Image Compression. Massachusetts institute of
technology artificial intelligence laboratory .A.I. Memo No. 1584.
Rafkind B., Lee M., Shih-Fu and Yu C. H. 2006. Exploring Text and
Image Features to Classify Images in Bioscience Literature. In
Proceedings of the BioNLP Workshop on Linking Natural Language
Processing and Biology at HLT- NAACL 06, pages 73–80, New York
City.
Roka A., Csapó Á., Reskó B., Baranyi P. 2007.Edge Detection Model
Based on Involuntary Eye Movements of the Eye-Retina System. Acta
Polytechnica Hungarica Vol. 4.
Basu M. 2002.Gaussian-Based Edge-Detection Methods—A Survey. In
proceedings of IEEE Transactions on systems, man and cybernetics-Part
C: Applications and reviews, Vol. 32, No. 3.
Perona P., Malik J. 1990. Scale-Space and Edge Detection Using
Anisotropic Diffusion. In proceedings of IEEE Transactions on pattern
analysis and machine intelligence, Vol. 12. No. 7.
Jiang X., Bunke H. 1999. Edge Detection in Range Images Based on
Scan Line Approximation. Computer Vision and Image Understanding
Vol. 73, No. 2, February, pp. 183–199.
Meer P. 2001. Edge Detection with Embedded Confidence. IEEE
Transactions on pattern analysis and machine intelligence, Vol. 23, No.
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