Texture Defect Detection Using Independent Vector Analysis in Wavelet Domain

• Published in: International Conference on Pattern Recognition pp. 1639 - 1644
• Main Authors: Sari, Leda, Ertuzun, Aysin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2014
• Subjects: Accuracy; independent component analysis; Independent vector analysis; Indexes; texture analysis; Training; Vectors; Wavelet analysis; wavelet transform; Wavelet transforms
• ISSN: 1051-4651
• DOI: 10.1109/ICPR.2014.290

In this paper, we address the problem of defect detection in textile images, and present a novel hybrid method where independent vector analysis, a statistical method, is combined with wavelet transformation, a spectral method. Independent vector analysis, a generalization of independent component analysis, uses vectorized signals, thus, enables exploiting multiple datasets and offers a fully multivariate analysis. In this study, the multiple datasets are generated by wavelet transforming the texture image blocks of a predetermined size, and consequently, sub bands generated provide the dependent multiple datasets which are jointly processed to extract information from the dependencies present among them. Furthermore, subject diversity is introduced by taking these image blocks from multiple images corresponding to different textures in the TILDA database during the training phase. From this viewpoint, the proposed method may also be interpreted as a combination of independent vector analysis and group independent component analysis models. The proposed independent vector analysis based method is compared with the independent component analysis based method and some improvement from performance point of view is observed. Considering the results obtained, the proposed method can be an alternative solution to the defect detection problem.