×

Detecting defects with image data. (English) Zbl 1445.62317

Summary: Quality control using continuous monitoring from images is emerging as an active research area. These applications require adaptive statistical techniques in order to detect and isolate process abnormalities. A novel approach is introduced for monitoring schemes in the setting of image data when the quality is associated with uniform pixel gray-scales. The proposed approach requires the definition of a statistic which takes into account both the spatial dependency and the changes in local variability. An application on paper surface demonstrates how the monitoring scheme performs in practical applications.

MSC:

62P30 Applications of statistics in engineering and industry; control charts
94A08 Image processing (compression, reconstruction, etc.) in information and communication theory

Software:

bootstrap
Full Text: DOI

References:

[1] Bendoudjema, D.; Pieczynski, W., Unsupervised image segmentation using triplet Markov fields, Comput. Vision Image Understanding, 99, 3, 476-498 (2005)
[2] Conradsen, K., Nielsen, B.K., 1987. Textural features in classification of digital images. In: Proceedings of the Second International Tampere Conference in Statistics, pp. 143-159.; Conradsen, K., Nielsen, B.K., 1987. Textural features in classification of digital images. In: Proceedings of the Second International Tampere Conference in Statistics, pp. 143-159.
[3] Conradsen, K.; Nilsson, G., Data dependent filters for edge enhancement of Landsat images, Comput. Vision, Graphics Image Process., 38, 101-121 (1987)
[4] Efron, B.; Tibshirani, R. J., An Introduction to the Bootstrap (1993), Chapman & Hall: Chapman & Hall New York · Zbl 0835.62038
[5] Epifanio, I.; Ayala, G., A random set view of texture classification, IEEE Trans. Image Processing, 11, 8, 859-867 (2002)
[6] Ersboll, B. K.; Conradsen, K., A strategy for grading natural materials using a two step classification procedure, SPIE, 1821, 318-329 (1992)
[7] Geman, G.; Geman, G., Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Mach. Intell., 6, 721-741 (1984) · Zbl 0573.62030
[8] Guo, R.; Pandit, S. M., Automatic threshold selection based on histogram modes and a discriminant criterion, Mach. Vision Appl., 10, 331-338 (1998)
[9] Heiler, M.; Schnörr, C., Natural image statistics for natural image segmentation, Internat. J. Comput. Vision, 63, 1, 5-19 (2005)
[10] Liu, S. S.; Jernigan, M., Texture analysis and discrimination in additive noise, Comput. Vision Graphics Image Understanding, 49, 52-67 (1990)
[11] Luceño, A.; Box, G., Statistical Control by Monitoring and Feedback Adjustment (1997), Wiley: Wiley New York · Zbl 0911.93002
[12] Mandriota, C.; Niti, C.; Ancona, N.; Stella, E.; Distante, R. M., Texture measures for carpet wear assessment, IEEE Trans. Pattern Anal. Mach. Intell., 10, 92-105 (2004)
[13] Mandriota, C.; Niti, C.; Ancona, N.; Stella, E.; Distante, R. M., Filter-based feature selection for rail defect detection, Mach. Vision Appl., 15, 179-185 (2004)
[14] Sahoo, P.; Soltani, S.; Wong, A.; Cheng, Y., A survey of thresholding techniques, Comput. Vision Graphics Image Process., 41, 2, 223-260 (1988)
[15] Siew, L. H.; Hodgson, R. M., Texture measures for carpet wear assessment, IEEE Trans. Pattern Anal. Mach. Intell., 10, 92-105 (1988)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.