Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives. <s> BIB001 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> "...a blend of erudition (fascinating and sometimes obscure historical minutiae abound), popularization (mathematical rigor is relegated to appendices) and exposition (the reader need have little knowledge of the fields involved) ...and the illustrations include many superb examples of computer graphics that are works of art in their own right." Nature <s> BIB002 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> The authors discuss the problem of textile fabric inspection using the visual textural properties of the fabric. The problem is to detect and locate the various kinds of defects that might be present in a given fabric sample based on an image of the fabric. Stochastic models are used to model the visual fabric texture. The authors use the Gaussian Markov random field to model the texture image of nondefective fabric. The inspection problem is cast as a statistical hypothesis testing problem on statistics derived from the model. The image of the fabric patch to be inspected is partitioned into nonoverlapping windows of size N*N where each window is classified as defective or nondefective based on a likelihood ratio test of size alpha . The test is recast in terms of the sufficient statistics associated with the model parameters. The sufficient statistics are easily computable for any sample. The authors generalize the test when the model parameters of the fabric are assumed to be unknown. > <s> BIB003 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Classification of natural surfaces for quality inspection is a step in the automation of some factory functions in industry. It is noted that, in cases of surfaces represented by random field images, some of their parameters can be useful for pattern recognition and texture analysis. Assuming the parameter values to be governed by some probability density law which takes into account the spatial distribution, a data structure can be built, starting at the original image, by gathering data properly, in a son-father order, to construct reduced-resolution versions of that image in an exponentially tapering pyramid of arrays of sizes 2/sup n/*2/sup n/, . . ., 2*2. Iterative pyramids are then built to update the node values based on their sons and fathers. Nodes are enhanced on different levels that feature no relationship to their parents and become probable roots for regions to be segmented on the lowest levels. A pyramid linking process then follows the root identification. This methodology was implemented and applied to segmenting some defects in calf leather. > <s> BIB004 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Abstract Textural features for pattern recognition are compared. The problem addressed is to determine which features optimize classification rate. Such features may be used in image segmentation, compression, inspection, and other problems in computer vision. Many textural features have been proposed in the literature. No large-scale objective comparative study has appeared. The goal is comparing and evaluating in a quantitative manner four types of features, namely Markov Random Field parameters, multi-channel filtering features, fractal based features, and co-occurrence features. Performance is assessed by the criterion of classification error rate with a Nearest Neighbor classifier and the Leave-One-Out estimation method using forward selection. Four types of texture are studied, two synthetic (fractal and Gaussian Markov Random Fields) and two natural (leather and painted surfaces). The results show that co-occurrence features perform best followed by the fractal features. However, there is no universally best subset of features. The feature selection task has to be performed for each specific problem to decide which feature of which type one should use. <s> BIB005 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Random field (RF) parameters can be subjects of a probability distribution useful for image segmentation. A multiresolution methodology, based on the node linking of a pyramid that takes into account some kind of a Gaussian distribution for node values and its geometry is applied to the RF parameters to segment the natural image segmented and enhance some of the immersed objects. Before applying the above described system to a case study (calf leather), a simulation for the multiresolution methodology is applied to a generated RF, having RF objects with different means.<<ETX>> <s> BIB006 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Considers a specific class of textures which are stochastic, possibly periodic, two-dimensional signals displaying fractal-like or self-similar characteristics. Most natural textures belong to this class. The authors explore the use of autoregressive (AR) processes on trees as texture model. This theory was proposed by Basseville et al. (1992) for multiscale signal analysis. The generalized lattice structures used for parametrization of AR processes on trees make computer implementation fast and efficient. The authors have done extensive experiments on texture generation and study the effect of reflection coefficients and model order on the quality of generated textures. > <s> BIB007 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> The fractal dimension of a texture has been used in the past as a segmentation feature, but since it cannot sufficiently describe enough textural characteristics, additional features are needed. In this paper we demonstrate that by combining the fractal dimension with a simple textural energy measure, a significant performance improvement is achieved compared to using each feature alone. The fractal dimension is computed using an efficient method that is also more accurate than most other popular methods, and the textural energy is easily computed using convolutional masks. Segmentation and classification of natural textures based on these two features is presented and the effect of additive noise is considered. <s> BIB008 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> In this paper the problem of using machine vision in quality inspection of textile fabrics is considered. A model based approach with Markov random fields (MRF) as the texture model and a new method based on Karhunen-Loeve transforms is studied for the defect inspection of textile fabrics. The results are illustrated on real fabric images, and the real time implementation of the MRF approach on a two TMS320C40 based parallel processing system is given. <s> BIB009 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Industrial vision systems must operate in real-time, produce a low false alarm rate and be flexible so as to accommodate changes in the manufacturing process easily. This work presents a system for fabric manufacturing inspection. This environment, like paper and birch wood board industries, has particular characteristics in which morphological feature extraction for automated visual inspection cannot be used. The utilization of fractal dimension is investigated for discriminating defective areas. The efficiency of this approach is illustrated in textile images for defect recognition (with overall 96% accuracy). While this may sound complex, the method is in fact simple enough to be suitable for PC implementation, as demonstrated in the present work, and utilization across the Word Wide Web. <s> BIB010 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> This paper discusses the application of 1D AR (autoregressive) method for defect detection in Web inspection systems. AR coefficients are obtained off-line and are used in real-time data processing. The algorithm was successfully simulated on synthesized and some real world defective samples. This method eliminates the need for traditional frame grabbers and can be implemented in minimum hardware mounted inside the camera. <s> BIB011 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Abstract Texture classification is an important topic in texture analysis. Texture classification has wide applications in remote sensing, computer vision, and image analysis. During the past years, several authors discussed to use multiresolution stochastic approaches to model textures. However, in these approaches, the highpass components which contain rich detailed information are lost. In this paper, we propose multiresolution MRF (MRMRF) modeling to describe textures. MRMRF modeling is a method trying to fuse filtering theory and MRF models. In the MRMRF modeling, highpass components are considered as well as lowpass components. “Brodatz texture database” is used in this paper for the experiments and Nearest Linear Combination (NLC) is used as measurement of distance to improve the recognition rate. The experimental results show that NLC has much better performance than Nearest Neighbor (NN) as the measurement in MRMRF modeling. <s> BIB012 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> This work presents an application of software engineering to fabric inspection. An inspection system has been developed for textile industries that aims automatic failure detection. Such as wood, paper and steel industries, this environment has particular characteristics in which surface defect detection is used for quality control. This system combines concept from software engineering and decision support. Detection of defects within the inspected texture is performed in a first step acquiring images by CCD cameras, then extracting texture features and, finally by classifiers being trained a priori on database of defective and non-defective samples. The extracted data depend on the type of method selected for image analysis. The used types are based on segmentation or fractal dimension. Two usual segmentation techniques were adapted and improved. A new algorithm was developed to calculate efficiently fractal dimension of textures. Experiments show the accuracy and applicability of the proposed techniques for a real factory environment. <s> BIB013 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Parametric image segmentation consists of finding a label field that defines a partition of an image into a set of nonoverlapping regions and the parameters of the models that describe the variation of some property within each region. A new Bayesian formulation for the solution of this problem is presented, based on the key idea of using a doubly stochastic prior model for the label field, which allows one to find exact optimal estimators for both this field and the model parameters by the minimization of a differentiable function. An efficient minimization algorithm and comparisons with existing methods on synthetic images are presented, as well as examples of realistic applications to the segmentation of Magnetic Resonance volumes and to motion segmentation. <s> BIB014 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate least squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features. <s> BIB015 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> This paper presents a novel approach for defect detection using a wavelet-domain Hidden Markov Tree (HMT)1 model and a level set segmentation technique. The background, which is assumed to contain homogeneous texture, is modeled off-line with HMT. Using this model, a region map of the defect image is produced on-line through likelihood calculations, accumulated in a coarse-to-fine manner in the wavelet domain. As expected, the region map is basically separated into two regions: 1) the defects, and 2) the background. A level-set segmentation technique is then applied to this region map to locate the defects. This approach is tested with images of defective fabric, as well as x-ray images of cotton with trash. The proposed method shows promising preliminary results, suggesting that it may be extended to a more general approach of defect detection. <s> BIB016 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Computer-vision-based automatic detection of fabric defects is one of the difficult one-class classification tasks in the real world. To overcome the incapacity of a single fractal feature in dealing with this task, multiple fractal features have been extracted in the light of the theory of and problems present in the box-counting method as well as the inherent characteristics of woven fabrics. Based on statistical learning theory, the up-to-date support vector data description (SVDD) is an excellent approach to the problem of one-class classification. A robust new scheme is presented in this paper for optimally selecting values of the parameters especially that of the scale parameter of the Gaussian kernel function involved in the training of the SVDD model. Satisfactory experimental results are finally achieved by jointly applying the extracted multiple fractal features and SVDD to the detection of defects from several datasets of fabric samples with different texture backgrounds. <s> BIB017 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> The objective of this paper is to propose a robust approach to building a computer vision system to detect and classify pavement defects based on features, such as the contour of feature (chain code histogram, Hu-moment), the shape of an object (length, width, area). In this paper, we present a method to build an automated system to detect and classify the different types of defects such as rupture of the road edge, potholes, subsidence depressions based on image processing techniques and machine learning methods. That system includes the following steps. First step is to detect defect position (ROI) then the defect is described by its features. Finally, each defect is classified these different defect features. In our approach the following algorithms have been using: Markov Random Fields and Graph cuts method for image segmentation, Random Forests algorithm for data classification. <s> BIB018 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Surface inspection is a critical step in ensuring the product quality in the steel-making industry. In order to relieve inspectors of laborious work and improve the consistency of inspection, much effort has been dedicated to the automated inspection using computer vision approaches over the past decades. However, due to non-uniform illumination conditions and similarity between the surface textures and defects, the present methods are usually applicable to very specific cases. In this paper a new framework for surface inspection has been proposed to overcome these limitations. By investigating the image formation process, a quantitative model characterizing the impact of illumination on the image quality is developed, based on which the non-uniform brightness in the image can be effectively removed. Then a simple classifier is designed to identify the defects among the surface textures. The significance of this approach lies in its robustness to illumination changes and wide applicability to different inspection scenarios. The proposed approach has been successfully applied to the real-time surface inspection of round billets in real manufacturing. Implemented on a conventional industrial PC, the algorithm can proceed at 12.5 frames per second with the successful detection rate being over 90% for turned and skinned billets. <s> BIB019 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Model-Based Approaches (MBAs) <s> Static analysis tools, automatically detecting potential source code defects at an early phase during the software development process, are diffusely applied in safety-critical software fields. However, alarms reported by the tools need to be inspected manually by developers, which is inevitable and costly, whereas a large proportion of them are found to be false positives. Aiming at automatically classifying the reported alarms into true defects and false positives, we propose a defect identification model based on machine learning. We design a set of novel features at variable level, called variable characteristics, for building the classification model, which is more fine-grained than the existing traditional features. We select 13 base classifiers and two ensemble learning methods for model building based on our proposed approach, and the reported alarms classified as unactionable (false positives) are pruned for the purpose of mitigating the effort of manual inspection. In this paper, we firstly evaluate the approach on four open-source C projects, and the classification results show that the proposed model achieves high performance and reliability in practice. Then, we conduct a baseline experiment to evaluate the effectiveness of our proposed model in contrast to traditional features, indicating that features at variable level improve the performance significantly in defect identification. Additionally, we use machine learning techniques to rank the variable characteristics in order to identify the contribution of each feature to our proposed model. <s> BIB020
Model-based methods are classified into three groups: (1) fractal models; (2) autoregressive models; and (3) random field models. Fractals play a significant role in the description of the natural surfaces with self-similar and irregular texture. They were firstly reported by Mandelbrot BIB002 . In BIB010 BIB013 , Conci and Proenca introduced a differential box-counting method with non-overlapping copies of images and achieved a 96% success rate on 80/75 defect-free/defective samples. Bu et al. BIB017 performed defect detection based on four fractal features and support vector data description on seven datasets of 14,378 defect free samples and 3222 defect samples with a 98.3% success rate. Kaneko  achieved 93.85% accuracy of classification on 65 samples of the Brodatz texture database  ; however, the method is computationally heavier compared to the technique presented in BIB008 . In a comparative study by Ohanian and Dubes BIB005 , the fractal method performed well against GLCMs, Gabor filters and MRF-based methods; however, its success heavily depends on the self-similarity of the texture and therefore it provides weaker performance. Markov random fields approaches  combine both statistical and structural information of context dependent entities, such as pixels depending on their neighbor pixels, and can be used in texture segmentation  BIB014 and classification problems BIB015 BIB012 . Cohen et al. BIB003 used Gaussian MRF (GMRF) to model defect-free texture on fabric images. They treated the method as a hypothesis testing problem on the statistics derived from the GMRF model. Six 256 × 256 testing images with various defects were divided into non-overlapping sub-blocks, where each block was classified as defective or non-defective. Although the detection success rate was high, the reliability of the testing is questionable due to a limited dataset of samples. Özdemir and Ercil BIB009 compared their MRF-based method in fabric inspection and a Karhunen-Loeve (KL)-based method; then, in  , the authors determined the competitiveness of the MRF model against other statistical and spectral based methods. In 2000, Baykut et al.  applied the aforementioned GMRF method in real-time application with a dedicated DSP system. In 2005, Chan et al. BIB016 proposed a wavelet-domain Hidden Markov Tree model with a level set segmentation technique. Recently, in BIB018 , the authors described a defect pavement detection method, and improved a quality of image segmentation by Markov random fields and Graph cuts method with an unsupervised Random Forest learning methodology for classification. Moradi and Zayed  developed a real-time application for defect detection in sewer tunnels by using Hidden Markov Model (HMM) and achieved 82.5% detection rate on 40 samples. The main concept of the autoregressive model (AR) is to characterize texture features based on the linear dependencies of pixels BIB001 . Serafim BIB004 BIB006 applied multiresolution pyramids for leather defect segmentation of natural images based on two-dimensional AR models. Basu and Lin BIB007 used a multi-scale AR texture model on tress for fabric samples, while, in BIB011 , the authors used one-dimensional AR and a CCD camera for a real-time web inspection. From these studies, it is possible to state that lightning is a crucial component of the inspections. Although the testing outcomes were very promising, no quantitative results were published at the end of the tests  . Recently, Zhang et al. BIB020 proposed a defect identification model based on machine learning, where they automatically classified the reported alarms into true defects and false positives. The authors designed a set of novel features at variable level, called variable characteristics, for building the classification model and selected 13 base classifiers and two ensemble learning methods for model building. They achieved an 83.36% average accuracy of classification. In BIB019 , the authors used a quantitative model characterizing the impact of illumination with a simple classifier and achieved a best of 94% accuracy in 1865 samples.