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Optical Engineering

Precise image alignment using cooperative neural-fuzzy networks with association rule mining-based evolutionary learning algorithm
Author(s): Chi-Yao Hsu; Yi-Chang Cheng; Sheng-Fuu Lin
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Paper Abstract

Precise image alignment is considered a critical issue in industrial visual inspection, since it performs an accurate pose to the object in inspected images. Recently, image alignment based on neural networks has become very popular due to its performance at speed. However, such a method has difficulty when applied to the alignment of images on a large range of affine transformation. To address this, a cooperative neural-fuzzy network (CNFN) with association rule mining-based evolutionary learning algorithm (ARMELA) is proposed. Unlike traditional neural network-based approaches, the proposed CNFN utilizes a coarse-to-fine alignment procedure to adapt image alignment to a larger range of affine transformation. The proposed ARMELA combines the self-adaptive method and association rules selection method to self-adjust the structure and parameters of the neural-fuzzy network. Furthermore, L2 regularization is adopted to control ARMELA such that the convergence speed increases. Experimental results show that the performance of the proposed scheme is superior to the traditional neural network methods in terms of accuracy and robustness.

Paper Details

Date Published: 12 March 2012
PDF: 16 pages
Opt. Eng. 51(2) 027006 doi: 10.1117/1.OE.51.2.027006
Published in: Optical Engineering Volume 51, Issue 2
Show Author Affiliations
Chi-Yao Hsu, National Chiao Tung Univ. (Taiwan)
Yi-Chang Cheng, National Chiao Tung Univ. (Taiwan)
Sheng-Fuu Lin, National Chiao Tung Univ. (Taiwan)

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