Competitive Hopfield Neural Network for Stereo Vision Correspondence

I
IJCNN
2000
IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5
Abstract - Competitive Hopfield Neural Network for Stereo Vision Correspondence

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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5

Como, Italy

July 24-July 27

ISBN: 0-7695-0619-4

	ASCII Text	x
	Rafael Mayoral, María José Pérez-Ilzarbe, "Competitive Hopfield Neural Network for Stereo Vision Correspondence," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 5, pp. 5464, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000.

	BibTex	x
	@article{ 10.1109/IJCNN.2000.861513, author = {Rafael Mayoral and María José Pérez-Ilzarbe}, title = {Competitive Hopfield Neural Network for Stereo Vision Correspondence}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {5}, year = {2000}, issn = {1098-7576}, pages = {5464}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861513}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on TI - Competitive Hopfield Neural Network for Stereo Vision Correspondence SN - 1098-7576 SP EP A1 - Rafael Mayoral, A1 - María José Pérez-Ilzarbe, PY - 2000 VL - 5 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER -

Rafael Mayoral, Universidad P?blica de Navarra

María José Pérez-Ilzarbe, Universidad P?blica de Navarra

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861513

For the correspondence problem in stereovision, w e dev eloped a Hopfield algorithm that favors unicity of the matches for all interest points both on the left and right images. Although the convergence of the network used cannot be theoretically proven, we have experimentally shown that for the cases we are interested in the method converges either to a stable state or to an acceptable limit cycle. The method is computationally fast.

Citation:

Rafael Mayoral, María José Pérez-Ilzarbe, "Competitive Hopfield Neural Network for Stereo Vision Correspondence," ijcnn, vol. 5, pp.5464, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000

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