• Gintarė Vaidelienė Kaunas University of Technology
  • Jonas Valantinas Kaunas University of Technology



automatic visual inspection, defect detection, discrete wavelets transforms, statistical data analysis, texture images


In this paper, a new Haar wavelet-based approach to the detection and localization of defects in grey-level texture images is presented. This new approach explores space localization properties of the discrete Haar wavelet transform (HT) and generates statistically-based parameterized texture defect detection criteria. The criteria provide 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 experiment analyses samples of ceramic tiles, glass samples, as well as fabric scraps, taken from real factory environment.


Chambon S, Gourraud C, Moliard JM, Nicolle P (2010). Road crack extraction with adapted filtering and Markov model-based segmentation: introduction and validation. Int Joint Conference on Computer Vision Theory and Applications, VISAPP, May 2010, France.

Chen S, Lin B, Han X, Liang X (2013). Automated inspection of engineering ceramic grinding surface damage based on image recognition. Int J Adv Manuf Tech 66:431 43.

Chuang WL, Chen CH, Yen JY, Hsu YL (2009). Using MPCA of spectra model for fault detection in a hot strip mill. J Mater Process Tech 209:4162 8.

Hu C, Min X, Yun H, Wang T, Zhang S (2011). Automatic detection of sound knots and loose knots on sugi using gray level co-occurrence matrix parameters. Ann Forest Sci 68:1077-83.

Hu GH (2015). Automated defect detection in textured surfaces using optimal elliptical Gabor filters. Optik 126:1331 40.

Hu GH, Zhang GH, Wang QH (2014). Automated defect detection in textured materials using wavelet-domain hidden Markov models. Opt Eng 53(9):093107. doi: 10.1117/1.OE.53.9.093107.

Karimi MH, Asemani D (2014). Surface defect detection in tiling Industries using digital image processing methods: Analysis and evaluation. Isa T 53:834 44.

Kim SC, Kang TJ (2006). Automated defect detection system using wavelet packet frame and Gaussian mixture model. J Opt Soc Am A 23(11):2690-701.

Kim SC, Kang TJ (2007). Texture classification and segmentation using wavelet packet frame and Gaussian mixture model. Pattern Recogn 40:1207 21.

Kwon BK, Won JS, Kang DJ (2015). Fast defect detection for various types of surfaces using random forest with VOV Features. Int J Precis Eng Man 16(5):965 70.

Lin HD (2007). Automated visual inspection of ripple defects using wavelet characteristic based multivariate statistical approach. Image Vision Comput 25:1785 801.

Lin HD (2009). Automated defect inspection of light-emitting diode chips using neural network and statistical approaches. Expert Syst Appl 36:219-26.

Liu X, Su ZWZ, Choi KF (2008). Slub extraction in woven fabric images using Gabor filters. Text Res J 78(4):320 5.

Nacereddine N, Hamami L, Tridi M, Oucief N (2007). Non-parametric histogram-based thresholding methods for weld defect detection in radiography. International J Electr, Comput, Energe, Electron Commun Eng 9(1):1401-5.

Ngan HYT, Pang GKH, Yung NHC (2011). Automated fabric defect detection – a review. Image Vision Comput 29:442-58.

Ralló M, Millán MS, Escofet J (2009). Unsupervised novelty detection using Gabor filters for defect segmentation in textures. J Opt Soc Am A 26(9):1967-76.

Sari L, Ertüzün A (2014). Texture defect detection using independent vector analysis in wavelet domain. 22nd Int Conf Pattern Recogn, Aug 24-28, 2014, Sweden, 1639-44.

Tolba AS (2012). A novel multiscale-multidirectional autocorrelation approach for defect detection in homogeneous flat surfaces. Mach Vision Appl 23:739 50.

Valantinas J, Kančelkis D, Valantinas R, Viščiūtė G (2013). Improving space localization properties of the discrete wavelet transform. Informatica-Lithuan 24(4):657 74.

Wong WK, Yuen CWM, Fan DD, Chan LK, Fung EHK (2009). Stitching defect detection and classification using wavelet transform and BP neural network. Expert Syst Appl, 36:3845 56.

Xie X (2008). Review of recent advances in surface defect detection using texture analysis techniques. ELCVIA 7(3):1-22.

Xie X, Mirmehdi M, Thomas B (2006). Colour tonality inspection using eigenspace features. Mach Vision Appl 16(6):364-73.

Yuan XC, Wua LS, Pengb Q (2015). An improved Otsu method using the weighted object variance for defect detection. Appl Surf Sci 349:472 84.




How to Cite

Vaidelienė, G., & Valantinas, J. (2016). THE USE OF HAAR WAVELETS IN DETECTING AND LOCALIZING TEXTURE DEFECTS. Image Analysis and Stereology, 35(3), 195–201.



Original Research Paper