Authors:
Gintarė Vaidelienė
and
Jonas Valantinas
Affiliation:
Kaunas University of Technology, Lithuania
Keyword(s):
Texture Images, Defect Detection, Discrete Wavelets Transforms, Statistical Data Analysis, Automatic Visual Inspection.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
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.