GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures

Chi-square histogram distance is one of the distance measures that can be used to find dissimilarity between two histograms. Motivated by the fact that texture discrimination by human vision system is based on second-order statistics, we make use of histogram of gray-level co-occurrence matrix (GLCM) that is based on second-order statistics and propose a new machine vision algorithm for automatic defect detection on patterned textures. Input defective images are split into several periodic blocks and GLCMs are computed after quantizing the gray levels from 0-255 to 0-63 to keep the size of GLCM compact and to reduce computation time. Dissimilarity matrix derived from chi-square distances of the GLCMs is subjected to hierarchical clustering to automatically identify defective and defect-free blocks. Effectiveness of the proposed method is demonstrated through experiments on defective real-fabric images of 2 major wallpaper groups (pmm and p4m groups).Comment: IJCVR, Vol. 2, No. 4, 2011, pp. 302-31

An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection

Similarity Measures for Automatic Defect Detection on Patterned Textures

Similarity measures are widely used in various applications such as information retrieval, image and object recognition, text retrieval, and web data search. In this paper, we propose similarity-based methods for defect detection on patterned textures using five different similarity measures, viz., Normalized Histogram Intersection Coefficient, Bhattacharyya Coefficient, Pearson Product-moment Correlation Coefficient, Jaccard Coefficient and Cosine-angle Coefficient. Periodic blocks are extracted from each input defective image and similarity matrix is obtained based on the similarity coefficient of histogram of each periodic block with respect to itself and other all periodic blocks. Each similarity matrix is transformed into dissimilarity matrix containing true-distance metrics and Ward’s hierarchical clustering is performed to discern between defective and defect-free blocks. Performance of the proposed method is evaluated for each similarity measure based on precision, recall and accuracy for various real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple, knot, and missing pick

Fabric Defect Identification Using Back Propagation Neural Networks

Fabric defect identification plays a very important role for the automatic detection in fabrics. Fabric defect identification mainly includes three parts: The first, preprocessing with Frequency domain Butterworth Low pass Filter and Histogram Equalization. The second, extraction of texture features from fabric using Gray Level Co-occurrence Matrix (GLCM).The Co-occurrence matrix characterizes the distribution of co-occurring pixel values in an image to be at a given offset, and then the statistical features are extracted from this matrix. The Third, the extracted GLCM features are used for the classification of the texture using Back Propagation Neural Network with different learning rules for their effectiveness comparison

The Defect Detection on The Patterned Textiles Using The Motif of The Pattern and The Symmetry Group

katedra: KHT; rozsah: 92The thesis is aimed at the quality management in the textile industry. Patterns which occur on the fabric as well as on the printed patterns on textiles can be classified among 17 plane symmetry groups. Each of symmetry groups adheres to definite rules under which it is created. The core of each pattern is a motif, which repeats in the pattern according to the given rules of the symmetry group. If the symmetry is affected by a defect, this should be detected. The defect can be recorded either by the visual inspection done by a human or it can be detected by scanning technology. In the experimental part of the thesis, algorithm is created being capable of detecting the damage of motif symmetry and finding defects in the patterned fabric. The achieved results could be applied in the quality management practice in the textile industry.Diplomová práce je zaměřena na řízení jakosti v textilním průmyslu. Vzory, které se vyskytují na tkaninách, ale i tištěných vzorech na textiliích, se dají zařadit do 17 rovinných grup symetrie. Každá z grup symetrie se řídí určitými pravidly, podle kterých je tvořena. Základem každého vzoru je motiv, který se podle daných pravidel grupy symetrie ve vzoru opakuje. Pokud je narušena symetrie ve vzoru nějakou vadou, měla by být detekována vada. Vada může být zachycena vizuální kontrolou prováděnou člověkem, nebo může být zachycena pomocí snímací techniky. V experimentální části diplomové práci je vytvořen algoritmus, který je schopen detekovat narušení symetrie motivu a nalézt vady vzoru na textiliích. Získané výsledky by bylo možné uplatnit v praxi při řízení jakosti v textilním průmyslu

Diffusion of tin from TEC-8 conductive glass into mesoporous titanium dioxide in dye sensitized solar cells

The photoanode of a dye sensitized solar cell is typically a mesoporous titanium dioxide thin film adhered to a conductive glass plate. In the case of TEC-8 glass, an approximately 500 nm film of tin oxide provides the conductivity of this substrate. During the calcining step of photoanode fabrication, tin diffuses into the titanium dioxide layer. Scanning Electron Microscopy and Electron Dispersion Microscopy are used to analyze quantitatively the diffusion of tin through the photoanode. At temperatures (400 to 600 °C) and times (30 to 90 min) typically employed in the calcinations of titanium dioxide layers for dye sensitized solar cells, tin is observed to diffuse through several micrometers of the photoanode. The transport of tin is reasonably described using Fick\u27s Law of Diffusion through a semi-infinite medium with a fixed tin concentration at the interface. Numerical modeling allows for extraction of mass transport parameters that will be important in assessing the degree to which tin diffusion influences the performance of dye sensitized solar cells

Technology 2002: The Third National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2002 Conference and Exposition, December 1-3, 1992, Baltimore, MD. Volume 2 features 60 papers presented during 30 concurrent sessions