4 October 2001 Statistical-learning-based object recognition algorithm for solder joint inspection
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Proceedings Volume 4564, Optomechatronic Systems II; (2001) https://doi.org/10.1117/12.444096
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States
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.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kyoungchul Koh, Kyoungchul Koh, H.J. Choi, H.J. Choi, Jae-Seon Kim, Jae-Seon Kim, Hyungsuck Cho, Hyungsuck Cho, } "Statistical-learning-based object recognition algorithm for solder joint inspection", Proc. SPIE 4564, Optomechatronic Systems II, (4 October 2001); doi: 10.1117/12.444096; https://doi.org/10.1117/12.444096

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