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Proceedings Paper

Statistical-learning-based object recognition algorithm for solder joint inspection
Author(s): Kyoungchul Koh; H.J. Choi; Jae-Seon Kim; Hyungsuck Cho
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Paper Abstract

As PCB components become more complex and smaller, the conventional inspection method using traditional ICT and function test show their limitations in application. On the contrary, the automatic optical inspection (AOI) gradually becomes the alternative in the PCB assembly line. In particular, the PCB inspection machines need more reliable and flexible object recognition algorithms for high inspection accuracy. The conventional AOI machines use the algorithmic approaches such as template matching. Fourier analysis, edge analysis, geometric feature recognition or optical character recognition, which mostly require much of teaching time and expertise of human operators. To solve this problem, in this paper, a statistical learning based part recognition method is proposed. The performance of the proposed approach is evaluated on numerous samples of real electronic part images. Experimental results demonstrate satisfactory performance and practical usefulness in PCB inspection processes.

Paper Details

Date Published: 4 October 2001
PDF: 8 pages
Proc. SPIE 4564, Optomechatronic Systems II, (4 October 2001); doi: 10.1117/12.444096
Show Author Affiliations
Kyoungchul Koh, Sunmoon Univ. (South Korea)
H.J. Choi, Sunmoon Univ. (South Korea)
Jae-Seon Kim, Sunmoon Univ. (South Korea)
Hyungsuck Cho, Korea Advanced Institute of Science and Technology (South Korea)


Published in SPIE Proceedings Vol. 4564:
Optomechatronic Systems II
Hyungsuck Cho, Editor(s)

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