Wavelet-based Defect Detection System for Grey-level Texture Images
Gintarė Vaidelienė, Jonas Valantinas
2016
Abstract
In this study, a new wavelet-based approach (system) to the detection of defects in grey-level texture images is presented. This new approach explores space localization properties of the discrete wavelet transform (DWT) and generates statistically-based parameterized defect detection criteria. The introduced system’s parameter provides the user with a possibility to control the percentage of both the actually defect-free images detected as defective and/or the actually defective images detected as defect-free, in the class of texture images under investigation. The developed defect detection system was implemented using discrete Haar and Le Gall wavelet transforms. For the experimental part, samples of ceramic tiles, as well as glass samples, taken from real factory environment, were used.
References
- Bissi, L., Baruffa. G., Placidi, P., Ricci, E., Scorzoni, A., 2013. Automated defect detection in uniform and structured fabrics using Gabor filters and PCA. Journal of Visual Communication and Image Representation, 24, 838-845.
- Bu, H. G., Wang, J., Huang, X. B., 2009. Fabric defect detection based on multiple fractal features and support vector data description. Engineering Applications of Artificial Intelligence, 22(2), 224-235.
- Bu, H.-G., Huang, X.-B., Wang, J., Chen, X., 2010. Detection of fabric defects by auto-regressive spectral analysis and support vector data description. Textile Research Journal, 80(7), 579-589.
- Chan, C., Pang, G., 2000. Fabric defect detection by Fourier analysis. IEEE Transactions on Industry Applications, 36(5), 1267-1276.
- Chen, J., Jain, A. K., 1988. A structural approach to identify defects in textural images. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Beijing.
- de Andrade, R. M., Eduardo, A. C., 2011. Methodology for automatic process of the fired ceramic tile's internal defect using ir images and artificial neural network. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 33(1), 67-73.
- Dogandzic, A., Eua-anant, N., Zhang, B. H., 2005. Defect detection using hidden Markov random fields. In AIP Conference Proceedings, 760, 704-711.
- Ghazini, M., Monadjemi, A., Jamshidi, K., 2009. Defect detection of tiles using 2D wavelet transform and statistical features. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 3(1), 89-92.
- Gururajan, A., Sari-Sarraf, H., Hequet, E. F., 2008. Statistical approach to unsupervised defect detection and multiscale localization in two texture images. Optical Engineering, 47(2).
- Han, Y., Shi, P., 2007. An adaptive level-selecting wavelet transform for texture defect detection. Image and Vision Computing, 25, 1239-1248.
- Iivarinen, J., 2000. Surface defect detection with histogrambased texture features. In Proc. SPIE 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision.
- Jia, H. B., Murphey, Y. L., Shi, J. J., Chang, T. S., 2014. An intelligent real-time vision system for surface defect detection. In Proceedings of the 17th International Conference on Pattern Recognition, 239-242.
- Jin, Y., Wang, Z. B., Zhu, L. Q., Yang, J. L., 2011. Research on in-line glass defect inspection technology based on Dual CCFL. Procedia Engineering, 15, 1797-1801.
- Karimi, M. H., Asemani, D., 2014. Surface defect detection in tiling industries using digital image processing methods: Analysis and evaluation. ISA Transactions, 53, 834-844.
- Kim, S. C., Kang, T. J., 2006. Automated defect detection system using wavelet packet frame and Gaussian mixture model. Journal of the Optical Society of America A-Optics Image Science and Vision, 23(11), 2690-2701.
- Kumar, A., 2008. Computer-vision-based fabric defect detection: a survey. IEEE Transactions on Industrial Electronics, 55(1), 348-363.
- Latif-Amet, A., Ertüzün, A., Erçil, A., 2000. An efficient method for texture defect detection: sub-band domain co-occurrence matrices. Image and Vision Computing, 18, 543-553.
- Li, Y. D., Ai, J. X., Sun C. Q., 2013. Online fabric defect inspection using smart visual sensors. Sensors, 13(4), 4659-4673.
- Lin, H. D., 2007. Automated visual inspection of ripple defects using wavelet characteristic based multivariate statistical approach. Image and Vision Computing, 25(11), 1785-1801.
- Mak, K. L., Peng, P., Yiu, K. F. C., 2009. Fabric defect detection using morphological filters. Image and Vision Computing, 27, 1585-1592.
- Mak, K. L., Peng, P., Yiu, K. F. C., 2013. Fabric defect detection using multi-level tuned-matched Gabor filters. Journal of Industrial and Management Optimization, 8(2), 325-341.
- Ngan, H. Y. T., Pang, G. K. H., Yung, N. H. C., 2011. Automated fabric defect detection - A review. Image and Vision Computing, 29, 442-458.
- Ngan, H. Y. T., Pang, G. K. H., Yung, N. H. C., Ng, M. K., 2005. Wavelet based methods on patterned fabric defect detection. Pattern Recognition, 38, 559-576.
- Nguyen, T. S., Begot, S., Duculty, F., Avila, M., 2011. Free-form anisotropy: a new method for crack detection on pavement surface images. In 18th IEEE International Conference on Image Processing.
- Raheja, J. L., Kumar, S., Chaudhary, A., 2013. Fabric defect detection based on GLCM and Gabor filter: a comparison. Journal for Light and Electron Optics, 124, 6469-6474.
- Timm, F., Barth, E., 2011. Non-parametric texture defect detection using Weibull features. In Proc. SPIE 7877, Image Processing: Machine Vision Applications.
- Tsai, D. M., Kuo, C. C., 2007. Defect detection in inhomogeneously textured sputtered surfaces using 3D Fourier image reconstruction. Machine Vision and Applications, 18(6), 383-400.
- Vaideliene, G., Valantinas, J., Ražanskas, P., 2016 (to appear). On the use of discrete wavelets in implementing controllable defect detection system for texture images. Information Technology and Control.
- Valantinas, J., Kancelkis, D., Valantinas, R., Višciute, G., 2013. Improving space localization properties of the discrete wavelet transform. Informatica, 24(4), 657- 674.
- Wen, W., Xia, A., 1999. Verifying edges for visual inspection purposes. Pattern Recognition Letters, 20, 315-328.
- Xie, X., 2008. Review of recent advances in surface defect detection using texture analysis techniques. Electronic Letters on Computer Vision and Image Analysis, 7(3), 1-22.
- Yuen, C. W. M., Wong, W. K., Qian, S. Q., Chan, L. K., Fung, E. H. K., 2009. A hybrid model using genetic algorithm and neural network for classifying garment defects. Expert Systems with Applications, 36, 2037-2047.
- Zhao, X. F., Yang, J., Zhang, G. B., 2012. Glass defect detection based on image processing. In 4th International Conference on Software Technology and Engineering, 155-159.
Paper Citation
in Harvard Style
Vaidelienė G. and Valantinas J. (2016). Wavelet-based Defect Detection System for Grey-level Texture Images . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 143-149. DOI: 10.5220/0005678901430149
in Bibtex Style
@conference{visapp16,
author={Gintarė Vaidelienė and Jonas Valantinas},
title={Wavelet-based Defect Detection System for Grey-level Texture Images},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={143-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005678901430149},
isbn={978-989-758-175-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Wavelet-based Defect Detection System for Grey-level Texture Images
SN - 978-989-758-175-5
AU - Vaidelienė G.
AU - Valantinas J.
PY - 2016
SP - 143
EP - 149
DO - 10.5220/0005678901430149