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Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : Efficient and accurate image alignment using TSK-type neuro-fuzzy network with data-miningbased evolutionary learning algorithm | Hsu et al. EURASIP Journal on Advances in Signal Processing 2011 2011 96 http asp.eurasipjournals.eom content 2011 1 96 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Efficient and accurate image alignment using TSK-type neuro-fuzzy network with data-mining-based evolutionary learning algorithm Chi-Yao Hsu Yi-Chang Cheng and Sheng-Fuu Lin 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. Keywords subpixel accuracy TSK-type neuro-fuzzy network data-mining based evolutionary learning algorithm regularized least square 1. Introduction Accurate and efficient image alignment is widely applied to many industrial applications such as automatic visual inspection factory automation and robotic machine vision. Among them visual inspection is usually required at finding a geometric transformation to align images. More specifically the geometric transformation is commonly used as an affine transformation .
