Title: Accurate and rapid alignment of laser scanned 3D surface using TSK-type neural-fuzzy network-based coarse-to-fine strategy
Authors: Chang, Jyun-Wei
Lin, Sheng-Fuu
Hsu, Chi-Yao
Department of Electrical and Computer Engineering
Keywords: Three-dimensional surface;TSK-type neural-fuzzy network;Principal component analysis;Coarse-to-fine alignment approach
Issue Date: 1-Oct-2012
Abstract: Aligning a laser scanned three-dimensional (3D) surface is considered a critical step in object recognition, shape analysis, and automatic visual inspection. Two major concerns for the alignment task are execution time and alignment accuracy. Recently, neural network-based methods have become very popular due to their high efficiency. However, such methods experience difficulty in reaching high accuracy because the use of principal component analysis (PCA) to perform coarse alignment causes a large alignment error. Thus, a TSK-type neural-fuzzy network (TNFN)-based coarse-to-fine 3D surface alignment scheme is proposed in the current paper. Compared with traditional neural network-based approaches, the proposed method provides a coarse-to-fine alignment approach to ensure the accurate pose estimated by TNFN in the coarse phase, as well the high alignment speed provided by TNFN-based surface modeling in the fine phase. Experimental results demonstrate the superior performance of the proposed 3D surface alignment system over existing systems. (C) 2012 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.optlaseng.2012.04.005
ISSN: 0143-8166
DOI: 10.1016/j.optlaseng.2012.04.005
Volume: 50
Issue: 10
Begin Page: 1450
End Page: 1458
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