The surface defects are broadly divided into two types. One is local textural irregularities which is the main concern for most visual surface inspection applications. The other is global deviation of colour and/or texture, where local pattern or texture does not exhibit abnormalities. Automatic surface inspection for quality control has largely employed graylevel image processing techniques, for example in textile and wafer inspection. There are rising demands in the quality control industry for colour analysis to fulfil its vital role in visual inspection, e.g. in ceramic tile manufacturing. In this work, we first systematically review recent advances in surface inspection using computer vision and image processing techniques, particularly those based on texture analysis methods. The aim is to review the state-of-the-art techniques for the purposes of visual inspection and decision making schemes that are able to discriminate the features extracted from normal and defective regions. Then, we present our work on developing texture analysis techniques in application to the detection of abnormalities in colour texture surfaces, in particular ceramic tile surfaces on which patterns are regularly of a random nature. Those abnormalities we concerned can be divided into two categories: colour tonality defects and textural abnormalities.
Example defects on different types of surfaces: Steel, stone, textile, wood, and ceramic tiles. |
Textural Defect Detection
The techniques used to inspect textural abnormalities are classified into four categories: statistical approaches, structural approaches, filter based methods, and model based approaches.
Tonality Inspection
In industrial quality inspection of colour textured surfaces, such as ceramic tiles or fabrics, it is also important to maintain consistent tonality during production. Tonality inspection can be carried out on both uniform pattern surfaces and randomly textured surfaces. Existing methods include:
Colour Texture Analysis for Defect Detection
Due to rising demand and practice of colour texture analysis in application to visual inspection, those works that are dealing with colour texture analysis are discussed separately. Most colour texture analysis techniques are borrowed from methods designed for graylevel images, such as co-occurrence matrices and LBP. This extension of graylevel texture analysis techniques to deal with colour images usually takes one of the following forms:
Other techniques independent of graylevel methods attempt to apply fully three dimensional models to analyse colour textures.
Classification and Novelty Detection
The primary goals of visual inspection are detection and classification. This involves choosing an appropriate decision making scheme which is usually referred to as pattern classification. Generally, this can be divided into supervised classification and unsupervised (or semi-supervised) classification.
Observations
Colour Tonality Inspection
Colour tonality refers to global chromatic appearance of a surface. Its variation from surface to surface may be understated, but becomes significant once the surfaces are placed together. A multidimensional histogramming method is developed to detect subtle tonality changes by incorporating local chromatic information into global chromatic characteristics. Principal component analysis (PCA) is used to reveal the nonlinear noise interference introduced by the imaging system. Vector directional processing and reference eigenspace feature selection are proposed to obtain salient colour tonality representation.
Flow chart of proposed method. |
Random Textured Surface Inspection
Textural quality inspection involves the detection and localisation of various chromatic and textural imperfections. We suggest that although some textures have a random appearance, there are textural primitives that govern the global appearance. A novel two-layer generative model is proposed to represent an image or a family of images. In this model, random (or regular) texture images in the first layer are assumed to be generated from a collection of texture exemplars, or texems, in the second layer. A bottom-up texem generation process is proposed based on pixel neighbourhoods. This local contextual analysis using texems is applied to a large set of graylevel ceramic tile images, in which graylevel analysis is sufficient to detect textural abnormalities. Then, different schemes are explored to extend graylevel texems to colour images. This results in two different formulations and inference procedures for the texem model with different computational complexity. Spatial detection and localisation accuracy of the proposed methods is measured and compared using texture collage images. The texem model is also compared against the multiscale, multidirectional Gabor filter for defect detection. Both the colour tonality inspection and textural defect detection methods are implemented in novelty detection schemes to cope with the variety and unpredictable nature of defective samples.
Example results - same texture family
Defective samples | |
Localised defects |
Example results - different textures
Defective samples | |||
Localised defects |
Example results - VisTex Collages
Original collage images | |
Escofet et al.’s method [Escofet'98] | |
Gray-level texems in RGB channels | |
Gray-level texems in PCA decorrelated RGB eigenchannels | |
Full color texem model |
Funding
This project was funded by the European Commission under grant G1RD-CT- 2002-00783-MONOTONE.
Publications