<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Local Correlation and Entropy Maps as Tools for Detecting Defects in Industrial Images

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

International Journal of Applied Mathematics and Computer Science

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...

Local Correlation and Entropy Maps as Tools for Detecting Defects in Industrial Images

Publié en ligne: 21 mars 2008

Pages: 41 - 47

DOI: https://doi.org/10.2478/v10006-008-0004-0

Mots clés
Defects detection, image processing, local correlation, entropy map

This content is open access.

Local Correlation and Entropy Maps as Tools for Detecting Defects in Industrial Images

Publié en ligne: 21 mars 2008

Pages: 41 - 47

DOI: https://doi.org/10.2478/v10006-008-0004-0

Mots clésDefects detection, image processing, local correlation, entropy map

This content is open access.

Mots clés
Defects detection, image processing, local correlation, entropy map