GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures (original) (raw)
Related papers
Automatic Detection of Texture Defects using Texture-Periodicity and Chi-square Histogram Distance
2011
In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the Jensen-Shannon Divergence, which is a symmetrized and smoothed version of the Kullback-Leibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention
Similarity measures for automatic defect detection on patterned textures
International Journal of Information and Communication Technology, 2012
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.
Automatic Detection of Defects on Periodically Patterned Textures
Journal of Intelligent Systems, 2011
Defect detection is a major concern in quality control of various products in industries. This paper presents two different machine-vision based methods for detecting defects on periodically patterned textures. In the first method, input defective image is split into several blocks of size same as the size of the periodic unit of the image and chi-square histogram distances of each periodic block with respect to itself and all other periodic blocks are calculated to get a dissimilarity matrix. This dissimilarity matrix is subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. The second method of defect detection is based on Universal Quality Index which is a measure of loss of correlation, luminance distortion and contrast distortion between any two signals. Quality indices of a periodic block with respect to itself and all other periodic blocks are calculated to get a similarity matrix containing quality indices. Specific variances of the periodic blocks are derived from the quality index matrix through orthogonal factor model based on eigen decomposition. These variances are subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results of experiments on real fabric images with defects show that the defect detection methods based on chi-square histogram distance and universal quality index yield a success rate of 98.6% and 97.8% respectively.
TEXTURE DEFECT DETECTION USING SUBBAND DOMAIN CO-OCCURRENCE MATRICES
In this paper, a new defect detection algorithm for textured images is presented. The algorithm is based on the subband decomposition of gray level images through wavelet filters and extraction of the co-occurrence features from the subband images. Detection of defects within the inspected texture is performed by partitioning the textured image into non-overlapping subwindows and classifying each subwindow as defective or nondefective with a mahalanobis distance classifier being trained on defect free samples a priori. The experimental results demonstrating the use of t h s algorithm for the visual inspection of textile products obtained from the real factory environment are also presented.
Texture Defect Detection in Gradient Space
2014
In this paper, we propose a machine vision algorithm for automatically detecting defects in patterned textures with the help of gradient space and its energy. Gradient space image is obtained from the input defective image and is split into several blocks of size same as that of the periodic unit of the input defective image. Energy of the gradient space image is used as feature space for identifying defective and nondefective periodic blocks using Ward’s hierarchical clustering. Experiments on real fabric images with defects show that the proposed method can be used for automatic detection of fabric defects in textile industries.
A new method for detecting texture defects based on modified local binary pattern
Signal, Image and Video Processing, 2018
The modified local binary pattern is a method that can produce high-precision features for detection and diagnosis of texture images; in this paper, a method is proposed to detect the texture defects based on this algorithm. The proposed method includes two main phases. The first phase is based on clustering technique to fabric normal texture modeling, and the second phase is a threshold to decide about the fabric defects selection. The total dataset in this research contains 596 texture images from different databases including Isfahan textile dataset, UHK dataset, products and TILDA dataset. The fabric defects are generated because of pressure cracks and has effects, woof defects, warp defects and spool slacking. Finally, a noticeable detection rate about 91.86% with a higher rate of 92.02% sensitivity is achieved for the total given dataset. All of the reported results from tests are achieved by applying the proposed method on the explained dataset.
2004
Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.
Defect Identification Using Texture Analysis
This paper focuses the major properties of Texture analysis. The properties are Coarseness, Contrast, Complexity, Strength, Energy, Entropy, Correlation and Homogeneity. These are defined conceptually in terms of spatial changes in intensity. These are then approximated in computational forms. Initially, the input image is divided into sub images and apply all the properties over the sub images. This is mainly, to identity the defect which is present in the input image. The result shows that if any defect present in the input image, will be identified easily. The application used is Department of Textile.
A Block-Processing Approach Using Texture Analysis for Fabric Defect Detection
Anais do XI Computer on the Beach - COTB '20
The quality control is an essential step in fabric industries. Detectdefects in the early stages can reduce costs and increase the qualityof the products. Currently, this task is mainly done by humans,whose judgment can be affected by fatigue. Computer vision-basedtechniques can automatically detect defects, reducing the need forhuman intervention. In this context, this work proposes an imageblock-processing approach, where we compare the Segmentation-Based Fractal Texture Analysis, Gray Level Co-Occurrence Matrix,and Local Binary Pattern in the feature extraction step. Aimingto show the efficiency of this approach for the problem, these resultswere compared with the same algorithms without the blockprocessingapproach. A Support Vector Machine optimized by Grid-Search Algorithm was used to classify the fabrics. The databaseused, which is available online, is composed of 479 images fromsamples with defects and without it. The results show that thisblock processing approach can improv...