Model Based Ceramic tile inspection using Discrete Wavelet Transform and Euclidean Distance

Visual inspection of industrial products is used to determine the control quality for these products. This paper deals with the problem of visual inspection of ceramic tiles industry using Wavelet Transform. The third level the coefficients of two dimensions Haar Discrete Wavelet Transform (HDWT) is used in this paper to process the images and feature extraction. The proposed algorithm consists of two main phases. The first phase is to compute the wavelet transform for an image free of defects which known as reference image, and the image to be inspected which known as test image. The second phase is used to decide whether the tested image is defected or not using the Euclidean distance similarity measure. The experimentation results of the proposed algorithm give 97% for correct detection of ceramic defects.Comment: Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

A texture segmentation prototype for industrial inspection applications based on fuzzy grammar

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

Texture segmentation based on fuzzy grammar for cork parquet quality control

This paper presents an approach for image texture segmentation based on the wavelets transform and on a fuzzy grammar inference system. It was developed for the Portuguese cork industry, specifically for the quality control in the cork parquet sector. The main purpose was to deal with major quality issues related with texture features. The segmentation procedure reveals a good performance indicated by high classification rates. This approach was integrated in a vision system leading to an industrial prototype that has already been tested, revealing good perspectives of full industrialization

Artificial Neural Network and Wavelet Features Extraction Applications in Nitrate and Sulphate Water Contamination Estimation

This work expounds the review of non-destructive evaluation using near-field sensors and its application in environmental monitoring. Star array configuration of planar electromagnetic sensor is explained in this work for nitrate and sulphate detection in water. The experimental results show that the star array planar electromagnetic sensor was able to detect nitrate and sulphate at different concentrations. Artificial Neural Networks (ANN) is used to classify different levels of nitrate and sulphate contaminations in water sources. The star array planar electromagnetic sensors were subjected to different water samples contaminated by nitrate and sulphate. Classification using Wavelet Transform (WT) was applied to extract the output signals features. These features were fed to ANN consequently, for the classification of different levels of nitrate and sulphate concentration in water. The model is capable of distinguishing the concentration level in the presence of other types of contamination with a root mean square error (RMSE) of 0.0132 or 98.68% accuracy

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

Cork parquet quality control vision system based on texture segmentation and fuzzy grammar

This paper presents a quality control vision system developed for the inspection of cork parquets that is already applied in the Portuguese cork industry. It is devoted specifically to the most critical quality issues: visibility of the lowest layer (BASE) on the noble layer (UPPER) and the homogeneity of this noble layer. Since these aspects are related with the texture of the raw material, the system was based on texture segmentation techniques. Features used were extracted from detail images of the wavelet transform. The classifier consists of a fuzzy grammar inference system. The segmentation procedure revealed a good performance indicated by high classification rates. Behavior in the industrial environment has been demonstrating high performance, revealing good perspectives for full spread industrialization

Adaptivna tehnika obrade slike za kontrolu kvalitete u proizvodnji keramičkih pločica

Automation of the visual inspection for quality control in production of materials with textures (tiles, textile, leather, etc.) is not widely implemented. A sophisticated system for image acquisition, as well as a fast and efficient procedure for texture analysis is needed for this purpose. In this paper the Surface Failure Detection (SFD) algorithm for quality control in ceramic tiles production is presented. It is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Networks (PNN) with radial basis. DWT provides a multi-resolution analysis, which mimics behavior of a human visual system and it extracts from the tile image the features important for failure detection. Neural networks are used for classification of the tiles with respect to presence of defects. Classification efficiency mainly depends on the proper choice of the training vectors for neural networks. For neural networks preparation we propose an automated adaptive technique based on statistics of the tiles defects textures. This technique enables fast adaptation of the SFD algorithm to different textures, which is important for automated visual inspection in the production of a new tile type.Automatizacija vizualne provjere za kontrolu kvalitete u proizvodnji materijala s teksturama (pločice, tekstil, kože, itd.) nije široko primijenjena u praksi. Za ovu namjenu potreban je sofisticirani sustav za snimanje slika, kao i brza i efikasna procedura za analizu tekstura. U ovom je radu predstavljen algoritam za detekciju površinskih oštećenja (SFD) u proizvodnji keramičkih pločica. Temelji se na diskretnoj valićnoj transformaciji (DWT) i probabilističkim neuronskim mrežama (PNN) s radijalnim bazama. DWT omogućava više-rezolucijsku analizu koja oponaša ljudski vizualni sustav i izdvaja iz slike pločice značajne za detekciju oštećenja. Neuronske mreže se koriste za klasifikaciju pločica ovisno o postojanju oštećenja. Efikasnost klasifikacije najviše ovisi o odgovarajućem odabiru vektora za učenje neuronskih mreža. Za pripremu neuronskih mreža predlažemo automatiziranu adaptivnu tehniku koja se temelji na statistici tekstura oštećenja na pločicama. Ova tehnika omogućava brzu adaptaciju SFD algoritma na različite teksture, što je posebno važno za automatiziranu vizualnu provjeru u proizvodnji novog tipa pločica