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Proceedings Paper

Automatic recognition and classifier of online parts based on machine vision
Author(s): Wenrong Wu; Dagui Huang; Fuzhi Wang; Sen Ge
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Paper Abstract

In order to call correct NC program automatically, real-time for corresponding online parts in the flexible manufacturing system (FMS), a new automatic recognition and classifier system based on machine vision was developed. In the image pre-processing, to make the extraction of image edge-detection better, a new re-filter, consisting of three steps-Gauss linear smoothness filter, sharpening, Median Filter, was first introduced. Then, Canny edge detection algorithm was adopted. Moreover, comparing with the most existing classification methods, such as Nearest Neighbor, Bayesian, Off- Line computations and so on, a new classification algorithm, Two Steps Shape Classification, was proposed. Using a Radial Feature Token (RFT), which functions as the ALISA Shape Module in the Adaptive Learning Image and Signal Analysis (ALISA) system hierarchy. Experimental results confirm that the image processing algorithm is effective and useful for real-timely recognizing and classifying online parts in the FMS.

Paper Details

Date Published: 21 March 2006
PDF: 5 pages
Proc. SPIE 6040, ICMIT 2005: Mechatronics, MEMS, and Smart Materials, 60400B (21 March 2006); doi: 10.1117/12.664143
Show Author Affiliations
Wenrong Wu, Univ. of Electronic Science and Technology of China (China)
Dagui Huang, Univ. of Electronic Science and Technology of China (China)
Fuzhi Wang, Univ. of Electronic Science and Technology of China (China)
Sen Ge, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 6040:
ICMIT 2005: Mechatronics, MEMS, and Smart Materials
Yunlong Wei; Kil To Chong; Takayuki Takahashi; Shengping Liu; Zushu Li; Zhongwei Jiang; Jin Young Choi, Editor(s)

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