A Novel Feature Extraction Method for Mechanical Part Recognition

Su Qun Cao; Ge Lan Yang; Quan Yin Zhu; Hai Fei Zhai

doi:10.4028/www.scientific.net/AMM.88-89.116

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 88-89A Novel Feature Extraction Method for Mechanical...

A Novel Feature Extraction Method for Mechanical Part Recognition

Abstract:

Mechanical part recognition has great significance in automated sorting and processing. The core of mechanical part recognition is the part image feature extraction. Thus, how to extract part features to meet the real time requirements for the automated production line has a crucial role. A novel feature extraction algorithm is presented for part image features in this paper. It aims to optimize the fuzzy Fisher criterion function to figure out two orthogonal optimal discriminant vectors in an unsupervised way. Based on these two vectors, the linear transformation from d-dimension to 2-dimension can be obtained. Experimental results on three mechanical parts show its effectiveness.

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Periodical:

Applied Mechanics and Materials (Volumes 88-89)

Pages:

116-121

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.88-89.116

Citation:

Cite this paper

Online since:

August 2011

Authors:

Su Qun Cao, Ge Lan Yang, Quan Yin Zhu, Hai Fei Zhai

Keywords:

Feature Extraction, Mechanical Part Recognition, Optimal Discriminant Vector, Unsupervised Learning

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