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

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...

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Veröffentlicht in:EURASIP journal on advances in signal processing Jg. 2011; H. 1; S. 1 - 22
Hauptverfasser: Hsu, Chi-Yao, Cheng, Yi-Chang, Lin, Sheng-Fuu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.12.2011
Springer Nature B.V
Schlagworte:
Algorithms
Alignment
Data mining
Engineering
Evolutionary
Fuzzy systems
Learning
Neural networks
Quantum Information Technology
Signal,Image and Speech Processing
Spintronics
subpixel accuracy
data-mining based evolutionary learning algorithm
regularized least square
TSK-type neuro-fuzzy network
ISSN:1687-6180, 1687-6172, 1687-6180
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Zusammenfassung: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.
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/1687-6180-2011-96