Edge detection using wavelets

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Edge detection using wavelets

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ACM Southeast Regional Conference archive
Proceedings of the 44th annual Southeast regional conference table of contents

Melbourne, Florida

SESSION: Database systems and computer vision table of contents

Pages: 649 - 654

Year of Publication: 2006

ISBN:1-59593-315-8

Authors

Evelyn Brannock	Georgia State University, Atlanta, Georgia
Michael Weeks	Georgia State University, Atlanta, Georgia

Publisher

ACM New York, NY, USA

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ABSTRACT

This paper studies the edge-detecting characteristics of the 2-D discrete wavelet transform. Our problem is to automatically detect edges. Since a common claim about the wavelet transform is that it splits images into an approximation and details, which contain edges, we use it in our experiments. First, to determine its efficacy, the 2-D discrete wavelet transform is compared to other common edge-detection methods. Also, a number of combinatorial methods for the octaves are examined in the comparison. Due to this work, a novel boundary-tracing algorithm is developed, to follow edges around an object of interest.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	The Math Works, Inc. Image Processing Toolbox User's Guide. The MathWorks, Inc, Natick, MA, 2004.
	2	A Classified and Comparative Study of Edge Detection Algorithms, Proceedings of the International Conference on Information Technology: Coding and Computing, p.117, April 08-10, 2002
	3	D. Marr and E. Hildreth. Theory of edge-detection. In Proceedings of the Royal Society of London. Series B, volume 207, pages 187--217, 1980.
	4	S. G. Mallat, A Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.11 n.7, p.674-693, July 1989 [doi>10.1109/34.192463 ]
	5	J Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.8 n.6, p.679-698, Nov. 1986
	6	Rafael C. Gonzalez , Richard E. Woods , Steven L. Eddins, Digital Image Processing Using MATLAB, Prentice-Hall, Inc., Upper Saddle River, NJ, 2003
	7	S. Mallat. Multifrequency channel decompositons of images and wavelet models. IEEE Trans. Acoust., Speech, Signal Processing, 37(12):2091--2110, December 1989.
	8	Wavelets: tools for science & Technology, Society for Industrial and Applied Mathematics, Philadelphia, PA, 2001