標題: Accurate and rapid alignment of laser scanned 3D surface using TSK-type neural-fuzzy network-based coarse-to-fine strategy
作者: Chang, Jyun-Wei
Lin, Sheng-Fuu
Hsu, Chi-Yao
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: Three-dimensional surface;TSK-type neural-fuzzy network;Principal component analysis;Coarse-to-fine alignment approach
公開日期: 1-十月-2012
摘要: 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
http://hdl.handle.net/11536/16841
ISSN: 0143-8166
DOI: 10.1016/j.optlaseng.2012.04.005
期刊: OPTICS AND LASERS IN ENGINEERING
Volume: 50
Issue: 10
起始頁: 1450
結束頁: 1458
顯示於類別:期刊論文


文件中的檔案:

  1. 000307037200015.pdf