FAST SEARCH ALGORITHMS FOR INDUSTRIAL INSPECTION

Author:

CHANG MING-CHING1,FUH CHIOU-SHANN1,CHEN HSIEN-YEI2

Affiliation:

1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC

2. Mechanical Industry Research Laboratories (MIRL), Industrial Technology Research Institute (ITRI), Hsinchu, Taiwan, ROC

Abstract

This paper presents an efficient general purpose search algorithm for alignment and an applied procedure for IC print mark quality inspection. The search algorithm is based on normalized cross-correlation and enhances it with a hierarchical resolution pyramid, dynamic programming, and pixel over-sampling to achieve subpixel accuracy on one or more targets. The general purpose search procedure is robust with respect to linear change of image intensity and thus can be applied to general industrial visual inspection. Accuracy, speed, reliability, and repeatability are all critical for the industrial use. After proper optimization, the proposed procedure was tested on the IC inspection platforms in the Mechanical Industry Research Laboratories (MIRL), Industrial Technology Research Institute (ITRI), Taiwan. The proposed method meets all these criteria and has worked well in field tests on various IC products.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Rapid visual positioning of sheet metal parts based on electronic drawing templates;Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics;2022-06-24

2. A real-time marking defect inspection method for IC chips;SPIE Proceedings;2015-12-09

3. Development of an Low Cost Platform for IC Printed Mark Defects Inspection;Lecture Notes in Electrical Engineering;2014

4. The development of automated solder bump inspection using machine vision techniques;The International Journal of Advanced Manufacturing Technology;2013-05-18

5. Time–frequency manifold correlation matching for periodic fault identification in rotating machines;Journal of Sound and Vibration;2013-05

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