Pattern Recognition Based on Relative Position of Local Features Using Self-Organizing Map

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First International Conference on Innovative Computing, Information and Control - Volume II (ICICIC'06)

Beijing, China

August 30-September 01

ISBN: 0-7695-2616-0

	ASCII Text	x
	Keiichi Horio, Akira Aikawa, Takeshi Yamakawa, "Pattern Recognition Based on Relative Position of Local Features Using Self-Organizing Map," Innovative Computing ,Information and Control, International Conference on, vol. 2, pp. 293-296, First International Conference on Innovative Computing, Information and Control - Volume II (ICICIC'06), 2006.

	BibTex	x
	@article{ 10.1109/ICICIC.2006.329, author = {Keiichi Horio and Akira Aikawa and Takeshi Yamakawa}, title = {Pattern Recognition Based on Relative Position of Local Features Using Self-Organizing Map}, journal ={Innovative Computing ,Information and Control, International Conference on}, volume = {2}, year = {2006}, isbn = {0-7695-2616-0}, pages = {293-296}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICICIC.2006.329}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Innovative Computing ,Information and Control, International Conference on TI - Pattern Recognition Based on Relative Position of Local Features Using Self-Organizing Map SN - 0-7695-2616-0 SP293 EP296 A1 - Keiichi Horio, A1 - Akira Aikawa, A1 - Takeshi Yamakawa, PY - 2006 KW - null VL - 2 JA - Innovative Computing ,Information and Control, International Conference on ER -

Keiichi Horio, Kyushu Institute of Technology, Japan

Akira Aikawa, Kyushu Institute of Technology, Japan

Takeshi Yamakawa, Kyushu Institute of Technology, Japan

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICICIC.2006.329

In this paper, we propose a new pattern recognition method based on relative position of local feature. In the visual system of human, the local features such as lines and curves are extracted, and they are integrated. In the proposed method, relative position of the gazing points which include local features are extracted using self-organizing map. A template matching concerning the local features are used for recognizing the patterns. The effectiveness of the proposed method is verified by applying it to MNIST handwritten digits database.

Citation:

Keiichi Horio, Akira Aikawa, Takeshi Yamakawa, "Pattern Recognition Based on Relative Position of Local Features Using Self-Organizing Map," icicic, vol. 2, pp.293-296, First International Conference on Innovative Computing, Information and Control - Volume II (ICICIC'06), 2006

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