МЕТОД РАСПОЗНАВАНИЯ ФИГУР С ИСПОЛЬЗОВАНИЕМ

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This paper provides a brief review on contour-based shape signatures. A new algorithm for shape
recognition based on Fourier descriptors and multilayer neural network is proposed. The paper also presents an analysis on the capabilities of the Fourier-descriptors as the input data for neural networks in
recognition of the complex shapes.
Key words: Fourier descriptors, shape recognition, neural networks, multilayer perceptron
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1. FOLKERS A., SAMET H. Content-based image retrieval using Fourier descriptors on a logo database //
Proc. of the 16th Intern. conf. on pattern recognition, Quebec (Canada), 11–15 Aug. 2002. Washington:
IEEE Computer Soc., 2002. V. 3. P. 521ದ 524.
2. ZHANG D., LU G. Review of shape representation and description techniques // Pattern Recognition.
2004. V. 37. P. 1–19.
3. NIXON M. Feature extraction and image processing / M. Nixon, A. Aguado. Oxford: Elsevier, 2008.
406 p.
4. PATTERN recognition techniques, technology and applications / Ed. by Peng-Yeng Yin. Croatia: InTech, 2008. 626 p.
5. GHUNEIM A. G. Moore-neighbor tracing // Contour Tracing. 2010. http://www.imageprocessingplace.
com/downloads_V3/root_downloads/tutorials/contour_tracing_Abeer_George_Ghuneim/moore.html.
6. FAUSETT L. V. Fundamentals of neural networks-architectures, algorithms, and applications. Upper
Saddle River: Prentice Hall, 1993. 461 p.
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; .: (382-2)70-16-09; e-mail: thangngt.cntt@gmail.com
– 02.11.11
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