Local correlation and entropy maps as tools for detecting defects in industrial images

• Autorzy: Ewa Skubalska-Rafajłowicz
• Języki publikacji: EN

Abstrakty

EN

The aim of this paper is to propose two methods of detecting defects in industrial products by an analysis of gray level images with low contrast between the defects and their background. An additional difficulty is the high nonuniformity of the background in different parts of the same image. The first method is based on correlating subimages with a nondefective reference subimage and searching for pixels with low correlation. To speed up calculations, correlations are replaced by a map of locally computed inner products. The second approach does not require a reference subimage and is based on estimating local entropies and searching for areas with maximum entropy. A nonparametric estimator of local entropy is also proposed, together with its realization as a bank of RBF neural networks. The performance of both methods is illustrated with an industrial image.

Słowa kluczowe

EN

defects detection; image processing; local correlation; entropy map

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2008
• Tom: 18
• Numer: 1
• Strony: 41-47

Daty

wydano

2008

Twórcy

autor

Ewa Skubalska-Rafajłowicz

• Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, ul. Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

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Identyfikatory

DOI

10.2478/v10006-008-0004-0