Efficient and accurate image alignment using TSK-type neuro-fuzzy network with data-mining-based evolutionary learning algorithm

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

doi:10.1186/1687-6180-2011-96

Abstract

Abstract Image alignment is considered a key problem in visual inspection applications. The main concerns for such tasks are fast image alignment with subpixel accuracy. About this, neural network-based approaches are very popular in visual inspection because of their high accuracy and efficiency of aligning images. However, such methods are difficult to identify the structure and parameters of neural network. In this study, a Takagi-Sugeno-Kang-type neuro-fuzzy network (NFN) with data-mining-based evolutionary learning algorithm (DMELA) is proposed. Compared with traditional learning algorithms, DMELA combines the self-organization algorithm (SOA), data-mining selection method (DMSM), and regularized least square (RLS) method to not only determine a suitable number of fuzzy rules, but also automatically tune the parameters of NFN. Experimental results are shown to demonstrate superior performance of the DMELA constructed image alignment system over other typical learning algorithms and existing alignment systems. Such system is useful to develop accurate and efficient image alignment systems.

Highlights

Accurate and efficient image alignment is widely applied to many industrial applications, such as automatic visual inspection, factory automation, and robotic machine vision
This study proposes a type neuro-fuzzy network (TNFN) with datamining-based evolutionary learning algorithm (DMELA) to solve the abovementioned problems
To explore the number of fuzzy rules for traditional symbiotic evolution (TSE) and multi-groups symbiotic evolution (MGSE), the fuzzy rules are tuned by setting the range of 20-100 in increments of 5

Summary

Introduction

Accurate and efficient image alignment is widely applied to many industrial applications, such as automatic visual inspection, factory automation, and robotic machine vision. There is a need to Regarding the aim, this study adopts weighted gradient orientation histograms (WGOH) [6] as an image descriptor, which extracts the features from inspected images, to be the input of the neural network. Such representation technique has been proven a good descriptor in several literatures [7,8]. The major contribution of this study is that the proposed learning method is helpful to develop efficient image alignment systems by automatically tuning the systems’ structure and parameters

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Conclusion