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Asian Conference on Computer Vision

ACCV 1995: Recent Developments in Computer Vision pp 363–371Cite as

Parallel distributed relaxation labeling

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1035))

Abstract

A parallel distributed relaxation labeling (RL) method, called the Lagrange-Hopfield (LH) method, is presented. RL is treated as a constrained optimization problem. The LH method solves the problem using the augmented Lagrangian multiplier technique and the graded Hopfield network. The LH method effectively overcomes instabilities that are inherent in the penalty method (e.g. Hopfield network) or the Lagrange multiplier method in constrained optimization. Due to the use of Lagrangian multipliers, the normalization operation in traditional RL methods is dispensed with. This makes the LH algorithm fully parallel and distributed and is suitable for analog implementation. Experiments also show that the method is able to produce good solutions in terms of the optimized objective values.

This work is supported by NTU project ARC-1/94.

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Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    S. Z. Li

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  1. S. Z. Li
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Editor information

Stan Z. Li Dinesh P. Mital Eam Khwang Teoh Han Wang

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© 1996 Springer-Verlag Berlin Heidelberg

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Li, S.Z. (1996). Parallel distributed relaxation labeling. In: Li, S.Z., Mital, D.P., Teoh, E.K., Wang, H. (eds) Recent Developments in Computer Vision. ACCV 1995. Lecture Notes in Computer Science, vol 1035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60793-5_90

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  • DOI: https://doi.org/10.1007/3-540-60793-5_90

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60793-9

  • Online ISBN: 978-3-540-49448-5

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