Precise image alignment using cooperative neural-fuzzy networks with association rule mining-based evolutionary learning algorithm

Chi Yao Hsu, Yi Chang Cheng, Sheng-Fuu Lin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Article number027006
JournalOptical Engineering
Volume51
Issue number2
DOIs
StatePublished - 1 Feb 2012

Keywords

  • Association rule mining
  • Cooperative neural-fuzzy network
  • L2 regularization
  • Self-adaptive method

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