Applying Texture Analysis to Industrial Inspection

En: Proceedings of the …, 2001

Nowadays, quality control is an important problem for fabric manufacturers. Typically these operations have been carried out by humans operators. However, this method has numerous drawbacks such as low precision, performance and effectiveness. Therefore, automatic inspection ...

Journal of Multimedia, 2008

This work presents an approach for color-texture classification of industrial products. An extension of Gray Level Co-occurrence Matrix (GLCM) to color images is proposed. Statistical features are computed from an isotropic Color Co-occurrence Matrix for classification. The following color spaces are used: RGB, HSL and La*b*. New combination schemes for texture analysis are introduced. A comparison with Local Binary Patterns (LBP) is also performed. The tests were conducted in a variety of industrial samples. The obtained results are promising and show the possibility of efficiently classifying complex industrial products based on color and texture features.

SPIE Proceedings, 2009

Automatic inspection of manufactured products with natural looking textures is a challenging task. Products such as tiles, textile, leather, and lumber project image textures that cannot be modeled as periodic or otherwise regular; therefore, a stochastic modeling of local intensity distribution is required. An inspection system to replace human inspectors should be flexible in detecting flaws such as scratches, cracks, and stains occurring in various shapes and sizes that have never been seen before. A computer vision algorithm is proposed in this paper that extracts local statistical features from grey-level texture images decomposed with wavelet frames into subbands of various orientations and scales. The local features extracted are second order statistics derived from grey-level co-occurrence matrices. Subsequently, a support vector machine (SVM) classifier is trained to learn a general description of normal texture from defect-free samples. This algorithm is implemented in LabVIEW and is capable of processing natural texture images in real-time.

1998

Quality control is one of the basic issues in textile industry. Texture analysis plays an important role in the automated visual inspection of texture images to detect their defects. For this purpose, model-based and featurebased methods are implemented and tested for textile images in a laboratory environment. The methods are compared in terms of their success rates in determining the defects.

Optical Inspection and Metrology for Non-Optics Industries, 2009

Automatic inspection of manufactured products with natural looking textures is a challenging task. Products such as tiles, textile, leather, and lumber project image textures that cannot be modeled as periodic or otherwise regular; therefore, a stochastic modeling of local intensity distribution is required. An inspection system to replace human inspectors should be flexible in detecting flaws such as scratches, cracks, and stains occurring in various shapes and sizes that have never been seen before. A computer vision algorithm is proposed in this paper that extracts local statistical features from grey-level texture images decomposed with wavelet frames into subbands of various orientations and scales. The local features extracted are second order statistics derived from grey-level co-occurrence matrices. Subsequently, a support vector machine (SVM) classifier is trained to learn a general description of normal texture from defect-free samples. This algorithm is implemented in LabVIEW and is capable of processing natural texture images in real-time.

Advances in Optical Technologies, 2013

We present an overview of methods and applications of automatic characterization of the appearance of materials through colour and texture analysis. We propose a taxonomy based on three classes of methods (spectral, spatial, and hybrid) and discuss their general advantages and disadvantages. For each class we present a set of methods that are computationally cheap and easy to implement and that was proved to be reliable in many applications. We put these methods in the context of typical industrial environments and provide examples of their application in the following tasks: surface grading, surface inspection, and content-based image retrieval. We emphasize the potential benefits that would come from a wide implementation of these methods, such as better product quality, new services, and higher customer satisfaction.

Real-Time Imaging, 2000

T exture analysis plays an important role in the automated visual inspection of textured images to detect their defects. For this purpose, model-based and feature-based methods are implemented and tested for textile images in a laboratory environment. The methods are compared in terms of their success rates in determining the defects. The Markov Random Field model is applied on dierent DSP systems for real-time inspection.

Handbook of Pattern Recognition and Computer Vision, 1993

This chapter reviews and discusses various aspects of texture analysis. The concentration is on the various methods of extracting textural features from images. The geometric, random field, fractal, and signal processing models of texture are presented. The major classes of texture processing problems such as segmentation, classification, and shape from texture are discussed. The possible application areas of texture such as automated inspection, document processing, and remote sensing are summarized. A bibliography is provided at the end for further reading.

Journal of Intelligent Manufacturing, 2016

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Sensor Review, 2009

Purpose -The purpose of this paper is to propose a set of techniques, in the domain of texture analysis, dedicated to the classification of industrial textures. One of the main purposes was to deal with a high diversity of textures, including structural and highly random patterns. Design/methodology/approach -The global system includes a texture segmentation phase and a classification phase. The approach for image texture segmentation is based on features extracted from wavelets transform, fuzzy spectrum and interaction maps. The classification architecture uses a fuzzy grammar inference system. Findings -The classifier uses the aggregation of features from the several segmentation techniques, resulting in high flexibility concerning the diversity of industrial textures. The resulted system allows on-line learning of new textures. This approach avoids the need for a global re-learning of the all textures each time a new texture is presented to the system. Practical implications -These achievements demonstrate the practical value of the system, as it can be applied to different industrial sectors for quality control operations. Originality/value -The global approach was integrated in a cork vision system, leading to an industrial prototype that has already been tested. Similarly, it was tested in a textile machine, for a specific fabric inspection, and gave results that corroborate the diversity of possible applications. The segmentation procedure reveals good performance that is indicated by high classification rates, revealing good perspectives for full industrialization.