Abstract
In principle, the orange skin surface defect can be successfully detected with the use of a set of relatively simple image processing techniques. To assess the technical possibilities of classifying relatively small surfaces the Histogram of Oriented Gradients (HOG) and the Support Vector Machine were used for two sets of about 400 surface patches in each. Color, grey and binarized images were used in tests. For grey images the worst classification accuracy was 91% and for binarized images it was 99%. For color image the results were generally worse. The experiments have shown that the cell size in the HOG feature extractor should be not more than 4 by 4 pixels which corresponds to 0.12 by \(0.12\,\)mm on the object surface.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allwein, E.L., Schapire, R.E., Singer, Y.: Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113–141 (2000). http://www.jmlr.org/papers/v1/allwein00a
Armesto, L., Tornero, J., Herraez, A., Asensio, J.: Inspection system based on artificial vision for paint defects detection on cars bodies. In: 2011 IEEE International Conference on Robotics and Automation, pp. 1–4, May 2011. doi:10.1109/ICRA.2011.5980570
Bucur, V.: Techniques for high resolution imaging of wood structure: a review. Measur. Sci. Technol. 14(12), R91 (2003). doi:10.1088/0957-0233/14/12/R01
Chmielewski, L.J., Laszewicz-Śmietańska, K., Mitas, P., Orłowski, A., et al.: Defect detection in furniture elements with the Hough transform applied to 3D data. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds.) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. AISC, vol. 403, pp. 631–640. Springer, Cham (2016). doi:10.1007/978-3-319-26227-7_59
Chmielewski, L.J., Orłowski, A., Śmietańska, K., Górski, J., et al.: Detection of surface defects of type ‘orange skin’ in furniture elements with conventional image processing methods. In: Huang, F., Sugimoto, A. (eds.) PSIVT 2015. LNCS, vol. 9555, pp. 26–37. Springer, Heidelberg (2016). doi:10.1007/978-3-319-30285-0_3
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). doi:10.1007/BF00994018
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005). vol. 1, pp. 886–893, June 2005. doi:10.1109/CVPR.2005.177
Karras, D.A.: Improved defect detection using support vector machines and wavelet feature extraction based on vector quantization and SVD techniques. In: Proceedings of International Joint Conference on Neural Networks, vol. 3, pp. 2322–2327, July 2003. doi:10.1109/IJCNN.2003.1223774
Konieczny, J., Meyer, G.: Computer rendering and visual detection of orange peel. J. Coat. Technol. Res. 9(3), 297–307 (2012). doi:10.1007/s11998-011-9378-2
Laszewicz, K., Górski, J.: Control charts as a tool for the management of dimensional accuracy of mechanical wood processing (in Russian). Annals of Warsaw University of Life Sciences-SGGW, Forestry and Wood Technology 65, 88–92 (2008)
Laszewicz, K., Górski, J., Wilkowski, J.: Long-term accuracy of MDF milling process-development of adaptive control system corresponding to progression of tool wear. Eur. J. Wood Wood Prod. 71(3), 383–385 (2013). doi:10.1007/s00107-013-0679-2
Laszewicz, K., Górski, J., Wilkowski, J., Czarniak, P.: Analysis of dimensional accuracy of milling process. Wood Res. 58(3), 451–463 (2013)
Longuetaud, F., Mothe, F., Kerautret, B., et al.: Automatic knot detection and measurements from X-ray CT images of wood: a review and validation of an improved algorithm on softwood samples. Comput. Electron. Agric. 85, 77–89 (2012). doi:10.1016/j.compag.2012.03.013
Musat, E.C., Salca, E.A., Dinulica, F., et al.: Evaluation of color variability of oak veneers for sorting. BioResources 11(1), 573–584 (2015). doi:10.15376/biores.11.1.573-584
Otsu, M.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 1, 62–66 (1979). doi:10.1109/TSMC.1979.4310076
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chmielewski, L.J., Orłowski, A., Wieczorek, G., Śmietańska, K., Górski, J. (2017). Testing the Limits of Detection of the ‘Orange Skin’ Defect in Furniture Elements with the HOG Features. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-54430-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54429-8
Online ISBN: 978-3-319-54430-4
eBook Packages: Computer ScienceComputer Science (R0)