Development of an Edge-based Point Correlation Algorithm Avoiding Full Point Search in Visual Inspection System 


Vol. 11,  No. 3, pp. 327-336, Jun.  2004
10.3745/KIPSTB.2004.11.3.327


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  Abstract

For visual inspection system in real industrial environment, it is one of most important tasks to design fast and stable pattern matching algorithm. This paper presents an edge-based point correlation algorithm avoiding full search in visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties for applying to automated inspection system in factory environment. First of all, NGC algorithms need high time complexity and thus high performance hardware to satisfy real-time process. In addition, lighting condition in realistic factory environments is not stable and therefore intensity variation from uncontrolled lights gives many troubles for applying directly NGC as pattern matching algorithm. In this paper, we propose an algorithm to solve these problems from using thinned and binarized edge data and skipping full point search with edge-map analysis. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the time complexity. Matching edges instead of using original gray-level pixel data overcomes NGC problems and pyramid of edges also provides fast and stable processing. All proposed methods are proved from experiments using real images.

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  Cite this article

[IEEE Style]

D. J. Kang, M. J. Kim, M. S. Kim, E. J. Lee, "Development of an Edge-based Point Correlation Algorithm Avoiding Full Point Search in Visual Inspection System," The KIPS Transactions:PartB , vol. 11, no. 3, pp. 327-336, 2004. DOI: 10.3745/KIPSTB.2004.11.3.327.

[ACM Style]

Dong Joong Kang, Mun Jo Kim, Min Sung Kim, and Eung Joo Lee. 2004. Development of an Edge-based Point Correlation Algorithm Avoiding Full Point Search in Visual Inspection System. The KIPS Transactions:PartB , 11, 3, (2004), 327-336. DOI: 10.3745/KIPSTB.2004.11.3.327.