Accurate and rapid alignment of laser scanned 3D surface using TSK-type neural-fuzzy network-based coarse-to-fine strategy

Jyun Wei Chang, Sheng-Fuu Lin*, Chi Yao Hsu

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

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.

Original languageEnglish
Pages (from-to)1450-1458
Number of pages9
JournalOptics and Lasers in Engineering
Volume50
Issue number10
DOIs
StatePublished - 1 Oct 2012

Keywords

  • Coarse-to-fine alignment approach
  • Principal component analysis
  • Three-dimensional surface
  • TSK-type neural-fuzzy network

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