Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> A theory of edge detection is presented. The analysis proceeds in two parts. (1) Intensity changes, which occur in a natural image over a wide range of scales, are detected separately at different scales. An appropriate filter for this purpose at a given scale is found to be the second derivative of a Gaussian, and it is shown that, provided some simple conditions are satisfied, these primary filters need not be orientation-dependent. Thus, intensity changes at a given scale are best detected by finding the zero values of ∇ 2 G(x, y) * I(x, y) for image I, where G(x, y) is a two-dimen­sional Gaussian distribution and ∇ 2 is the Laplacian. The intensity changes thus discovered in each of the channels are then represented by oriented primitives called zero-crossing segments, and evidence is given that this representation is complete. (2) Intensity changes in images arise from surface discontinuities or from reflectance or illumination bound­aries, and these all have the property that they are spatially localized. Because of this, the zero-crossing segments from the different channels are not independent, and rules are deduced for combining them into a description of the image. This description is called the raw primal sketch. The theory explains several basic psychophysical findings, and the opera­tion of forming oriented zero-crossing segments from the output of centre-surround ∇ 2 G filters acting on the image forms the basis for a physiological model of simple cells (see Marr & Ullman 1979). <s> BIB001 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Abstract Most vision research embracing the spatial frequency paradigm has been conceptually and mathematically a one-dimensional analysis of two-dimensional mechanisms. Spatial vision models and the experiments sustaining them have generally treated spatial frequency as a one-dimensional variable, even though receptive fields and retinal images are two-dimensional and linear transform theory obliges any frequency analysis to preserve dimension. Four models of cortical receptive fields are introduced and studied here in 2D form, in order to illustrate the relationship between their excitatory/inhibitory spatial structure and their resulting 2D spectral properties. It emerges that only a very special analytic class of receptive fields possess independent tuning functions for spatial frequency and orientation; namely, those profiles whose two-dimensional Fourier Transforms are expressible as the separable product of a radial function and an angular function. Furthermore, only such receptive fields would have the same orientation tuning curve for single bars as for gratings. All classes lacking this property would describe cells responsive to different orientations for different spatial frequencies and vice versa; this is shown to be the case, for example, for the Hubel & Wiesel model of cortical orientation-tuned simple cells receiving inputs from an aligned row of center/surround LGN cells. When these results are considered in conjunction with psychophysical evidence for nonseparability of spatial frequency and orientation tuning properties within a “channel”, it becomes mandatory that future spatial vision research of the Fourier genre take on an explicitly two-dimensional character. <s> BIB002 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> A new method for the inspection of textile webs is proposed. The normal web structure is characterized by the micro-texture of 3x3 neighbourhoods which is extracted by principal components. Local 'rectification' of principal component images yields feature planes which can be fed to a classificator. Preliminary results are presented.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. <s> BIB003 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> This paper proposes a general system approach applicable to the automatic inspection of textured material. First, the input image is preprocessed in order to be independent of non-uniformities. A tone-to-texture transform is then performed by mapping the original grey level picture on a multivariate local feature sequence, which turns out to be normally distributed. More specifically, features derived with the help of the Karhunen-Loeve decomposition of a small neighbourhood of each pixel are used. A decision as to conformity with a reference texture is arrived at by thresholding the Mahalanobis distance for every realization of the feature vector. It is shown that this approach is optimum under the Gaussian assumption in the sense that it has a minimum acceptance region for a fixed probability of false rejection. <s> BIB004 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Abstract Two-dimensional Gabor filters are used to segment images into regions of specific spatial frequency or orientation characteristic. The images are transformed into a modulated narrowband signal whose envelope coincides with the region(s) whose characteristics the filter is tured to. <s> BIB005 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. > <s> BIB006 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> For fast inspection of defects in textile fabric the complexity of calculations has to be reduced, in order to limit the system costs. Additionally algorithms which are suitable for migration into hardware have to be chosen. Therefore in the paper a segmentation algorithm using first order statistics is applied. Preprocessing includes a logarithmic greyscale transformation to obtain insensitivity to illumination changes. Afterwards texture features are extracted by a set of linear filters, which consider local neighbourhood relations. The filtered images are evaluated by histograms being calculated on a window grid. Finally the histograms are classified by a perceptron net trained by backpropagation. An interactive teach-in program is provided to adapt the system to different kinds of textile fabric and appearances of defects. > <s> BIB007 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> A new method is proposed for fault detection and identification in complex systems. The technique of Fuzzy Wavelet Analysis is introduced which uses a fuzzified wavelet transform to analyze wide bandwidth fault features. A special attribute of this tool is its ability to employ localized time/frequency analysis of fuzzy data for fault detection and identification purposes. Performance measures of detectability and identifiability are defined to assist in assessing the performance of the algorithm. Performance improvement is achieved through a learning mechanism based on the detectability and identifiability measures. A fuzzy similarity measure is also introduced to reduce sensitivity to noise. The algorithm uses both on-line and off-line learning for designing and updating the rulebase. <s> BIB008 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> A fast system to reveal the presence and type of fabric defects during the weaving process is developed. Since the fabric is similar to a 2-D grid, its defects are clearly observed in the changes in its optical Fourier transform (OFT), which appears stationary while the fabric is moving across the loom. Previous work, based on the statistical parameters of the OFT, showed that the presence of faults can be detected when only global changes in the images are considered. We show that by selecting a small subset of pixels from the image as input to a neural network, fabric defects can not only be detected but also successfully identified. A knowledge-based system could conceivably be constructed to use this information to resolve problems with the loom in real time, without the need for operator intervention. <s> BIB009 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Gabor functions, which localize information in both the spatial and the frequency domains, are used as filters for the inspection of common local defects in textile webs. A variety of defects are analyzed in different fabrics and in every case the flaws are finally segmented from the background.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. <s> BIB010 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Many textures such as woven fabrics and composites have a regular and repeating texture. This paper presents a new method to capture the texture information using adaptive wavelet bases. Wavelets are compact functions which can be used to generate a multiresolution analysis. Texture constraints are used to adapt the wavelets to better characterize specific textures. An adapted wavelet basis has very high sensitivity to the abrupt changes in the texture structure caused by defects. This paper demonstrates how adaptive wavelet basis can be used to locate defects in woven fabrics. <s> BIB011 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> In this article we introduce our approach to exploit multiscale wavelet methods for texture defect detection. Several wavelet bases and decomposition algorithms are examined in regard of applicability, parameterization and computational costs. The article points out specific problems in localizing texture defects in multiscale wavelet representations. Besides the fast dyadic wavelet transform we demonstrate the application of the translation invariant a trous algorithm on texture samples. Feature extraction methods are proposed and examples of successful defect classification results are shown. <s> BIB012 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> This paper describes a vision-based fabric inspection system that accomplishes on-loom inspection of the fabric under construction with 100% coverage. The inspection system, which offers a scalable, open architecture, can be manufactured at relatively low cost using off-the-shelf components. While synchronized to the motion of the loom, the developed system first acquires very high-quality, vibration-free images of the fabric using either front or backlighting. Then the acquired images are subjected to a novel defect segmentation algorithm, which is based on the concepts of wavelet transform, image fusion and the correlation dimension. The essence of this segmentation algorithm is the localization of those events (i.e., defects) in the input images that disrupt the global homogeneity of the background texture. The efficacy of this algorithm, as well as the overall inspection system, has been tested thoroughly under realistic conditions. The system was used to acquire and to analyze more than 3700 images of fabrics that were constructed with two different types of yarn. In each case, the performance of the system was evaluated as an operator introduced defects from 26 categories into the weaving process. The overall detection rate of the presented approach was found to be 89% with a localization accuracy of 0.2 in. (i.e., the minimum defect size) and a false alarm rate of 2.5%. <s> BIB013 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> This paper describes a robust segmentation algorithm for the detection and localization of woven fabric defects. The essence of the presented segmentation algorithm is the localization of those events (i.e., defects) in the input images that disrupt the global homogeneity of the background texture. To this end, preprocessing modules, based on the wavelet transform and edge fusion, are employed with the objective of attenuating the background texture and accentuating the defects. Then, texture features are utilized to measure the global homogeneity of the output images. If these images are deemed to be globally nonhomogeneous (i.e., defects are present), a local roughness measure is used to localize the defects. The utility of this algorithm can be extended beyond the specific application in our work, that is, defect segmentation in woven fabrics. Indeed, in a general sense, this algorithm can be used to detect and to localize anomalies that reside in images characterized by ordered texture. The efficacy of this algorithm has been tested thoroughly under realistic conditions and as a part of an on-line fabric inspection system. Using over 3700 images of fabrics, containing 26 different types of defects, the overall detection rate of our approach was 89% with a localization accuracy of less than 0.2 inches and a false alarm rate of 2.5%. <s> BIB014 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Abstract In this paper we present a global approach for the automatic inspection of defects in directionally textured surfaces which arise in textile fabrics and machined surfaces. The proposed method does not rely on local features of textures. It is based on a global image restoration scheme using the Fourier transform. The line patterns of any directional textures in the spatial domain image are removed by detecting the high-energy frequency components in the Fourier domain image using a one-dimensional (1D) Hough transform, setting them to zero, and finally back-transforming to a spatial domain image. In the restored image, the homogeneous line region in the original image will have an approximately uniform gray level, whereas the defective region will be distinctly preserved. A statistical process control scheme is therefore used to set up the control limits for discriminating between defects and homogeneous line patterns. The experiments on a variety of textile fabrics, machined surfaces and natural wood have shown the effectiveness of the proposed method. <s> BIB015 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> The current procedure for the determination of fabric defects in the textile industry is performed by humans in the offline stage. The advantage of an online inspection system is not only defect detection and identification, but also quality improvement by a feedback control loop to adjust setpoints. This paper introduces a vision-based online fabric inspection methodology of woven textile fabrics. The proposed inspection system consists of hardware and software components. The hardware components consist of CCD array cameras, a frame grabber and appropriate illumination. The software routines capitalize upon vertical and horizontal scanning algorithms to reduce the 2-D image into a stream of 1-D data. The wavelet transform is used next to extract features that are characteristic of a particular defect. The signal to noise ratio (SNR) calculation based on the results of the wavelet transform is performed to measure any defects. The defect declaration is carried out by employing SNRs and scanning methods. Learning routines are called upon to optimize the wavelet coefficients. Test results from different types of defect and different styles of fabric demonstrate the effectiveness of the proposed inspection system. <s> BIB016 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> This paper investigates various approaches for automated inspection of textured materials using Gabor wavelet features. A new supervised defect detection approach to detect a class of defects in textile webs is proposed. Unsupervised web inspection using a multichannel filtering scheme is investigated. A new data fusion scheme to multiplex the information from the different channels is proposed. Various factors interacting the tradeoff for performance and computational load are discussed. This scheme establishes high computational savings over the previously proposed approaches and results in high quality of defect detection. Final acceptance of visual inspection systems depends on economical aspects as well. Therefore, a new low-cost solution for fast web inspection is also included in this paper. The experimental results conducted on real fabric defects for various approaches proposed in this paper confirm their usefulness. <s> BIB017 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> This paper presents an automatic system for steel quality assessment, by measuring textural properties of carbide distributions. In current steel inspection, specially etched and polished steel specimen surfaces are classified manually under a light microscope, by comparisons with a standard chart. This procedure is basically two-dimensional, reflecting the size of the carbide agglomerations and their directional distribution. To capture these textural properties in terms of image fea tures, we first apply a rich set of image-processing operations, including mathematical morphology, multi-channel Gabor filtering, and the computation of texture measures with automatic scale selection in linear scale-space. Then, a feature selector is applied to a 40-dimensional feature space, and a classification scheme is defined, which on a sample set of more than 400 images has classification performance values comparable to those of human metallographers. Finally, a fully automatic inspection system is designed, which actively selects the most salient carbide structure on the specimen surface for subsequent classification. The feasibility of the overall approach for future use in the production process is demonstrated by a prototype system. It is also shown how the presented classification scheme allows for the definition of a new reference chart in terms of quantitative measures. <s> BIB018 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> The problem of automated defect detection in textured mate- rials is investigated. A new algorithm based on multichannel filtering is presented. The texture features are extracted by filtering the acquired image using a filter bank consisting of a number of real Gabor functions, with multiple narrow spatial frequency and orientation channels. For each image, we propose the use of image fusion to multiplex the infor- mation from sixteen different channels obtained in four orientations. Adaptive degrees of thresholding and the associated effect on sensitivity to material impurities are discussed. This algorithm realizes large com- putational savings over the previous approaches and enables high- quality real-time defect detection. The performance of this algorithm has been tested thoroughly on real fabric defects, and experimental results have confirmed the usefulness of the approach. © 2000 Society of Photo- Optical Instrumentation Engineers. (S0091-3286(00)01912-7) <s> BIB019 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> In this paper we present a machine vision system for the automatic inspection of defects in textured surfaces found in industry. The defects to be inspected are those that appear as local anomalies embedded in a homogeneous texture. The proposed method is based on a Gabor filtering scheme that computes the output response of energy from the convolution of a textured image with a specific Gabor filter. The best parameters of a Gabor filter are selected so that the energy of the homogeneous texture is zero, and any unpredictable defeats will generate significantly large energy values. A simple thresholding scheme then follows to discriminate between homogeneous regions and defective regions in the filtered image. This transforms texture differences into detectable filter output. The experiments on structural textures such as leather and sandpaper have shown the effectiveness of the proposed method. <s> BIB020 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Gabor filters have proved to be an effective segmentation and flaw detection tool. This study addresses the issue of an optimal 2-D Gabor filter design for automatically detecting defects in homogeneously textured woven fabrics. The parameters of these filters are derived through an optimisation process performing the minimisation of a Fisher cost function. By constraining some of the Gabor filter parameters to specific values the aim is to optimise the filter to detect a certain type of flaw as it appears in a particular textile background. To account for the potentially large variety of flaw types, the optimal parameters for multiple sets of constraints are computed. The detection outcomes from each set of optimal filters are combined to produce a final classification result. Successful detection results (with low false alarm rates) suggest that this optimal Gabor filter approach is a promising method for automated detection of flaws in homogenous textiles. <s> BIB021 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Abstract In this paper, an efficient algorithm, which combines concepts from wavelet theory and co-occurrence matrices, is presented for detection of defects encountered in textile images. Detection of defects within the inspected texture is performed first by decomposing the gray level images into sub-bands, then by partitioning the textured image into non-overlapping sub-windows and extracting the co-occurrence features and finally by classifying each sub-window as defective or non-defective with a Mahalanobis distance classifier being trained on defect free samples a priori. The experimental results demonstrating the use of this algorithm for the visual inspection of textile products obtained from the real factory environment are also presented. Experiments show that focusing on a particular band with high discriminatory power improves the detection performance as well as increases the computational efficiency. <s> BIB022 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Abstract In this paper, we present a multiresolution approach for the inspection of local defects embedded in homogeneous textured surfaces. The proposed method does not rely on the extraction of local textural features in a pixel-by-pixel basis. It is based on an efficient image restoration scheme using the wavelet transforms. By properly selecting the smooth subimage or the combination of detail subimages in different decomposition levels for backward wavelet transform, the reconstructed image will remove regular, repetitive texture patterns and enhance only local anomalies. Then, a simple thresholding can be used to discriminate between defective regions and homogeneous regions in the reconstructed image. This converts the difficult defect detection in complicated textured images into a simple binary thresholding in nontextured images. Effects of different wavelet bases, the number of multiresolution levels, the decomposed subimages used for reconstruction, and the change in image rotation on defect detection are thoroughly evaluated and discussed based on the experiment on a variety of real textures including machined surfaces, natural wood, textile fabrics, sandpaper and leather. <s> BIB023 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Abstract The task of detecting flaws in woven textiles can be formulated as the problem of segmenting a “known” non-defective texture from an “unknown” defective texture. In order to discriminate defective texture pixels from non-defective texture pixels, optimal 2-D Gabor filters are designed such that, when applied to non-defective texture, the filter response maximises a Fisher cost function. A pixel of potentially flawed texture is classified as defective or non-defective based on the Gabor filter response at that pixel. The results of this optimised Gabor filter classification scheme are presented for 35 different flawed homogeneous textures. These results exhibit accurate flaw detection with low false alarm rate. Potentially, our novel optimised Gabor filter method could be applied to the more complicated problem of detecting flaws in jacquard textiles. This second and more difficult problem is also discussed, along with some preliminary results. <s> BIB024 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> This paper proposes a new method for fabric defect classification by incorporating the design of a wavelet frames based feature extractor with the design of a Euclidean distance based classifier. Channel variances at the outputs of the wavelet frame decomposition are used to characterize each nonoverlapping window of the fabric image. A feature extractor using linear transformation matrix is further employed to extract the classification-oriented features. With a Euclidean distance based classifier, each nonoverlapping window of the fabric image is then assigned to its corresponding category. Minimization of the classification error is achieved by incorporating the design of the feature extractor with the design of the classifier based on minimum classification error (MCE) training method. The proposed method has been evaluated on the classification of 329 defect samples containing nine classes of fabric defects, and 328 nondefect samples, where 93.1% classification accuracy has been achieved. <s> BIB025 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> We propose a new method for fabric defect detection by in- corporating the design of an adaptive wavelet-based feature extractor with the design of an Euclidean distance-based detector. The proposed method characterizes the fabric image with multiscale wavelet features by using undecimated discrete wavelet transforms. Each nonoverlapping window of the fabric image is then detected as defect or nondefect with an Euclidean distance-based detector. Instead of using the standard wavelet bases, an adaptive wavelet basis is designed for the detection of fabric defects. Minimization of the detection error is achieved by incor- porating the design of the adaptive wavelet with the design of the detec- tor parameters using a discriminative feature extraction (DFE) training method. The proposed method has been evaluated on 480 defect samples from five types of defects, and 480 nondefect samples, where a 97.5% detection rate and 0.63% false alarm rate were achieved. The evaluations were also carried out on unknown types of defects, where a 93.3% detection rate and 3.97% false alarm rate were achieved in the detection of 180 defect samples and 780 nondefect samples. © 2002 <s> BIB026 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Abstract In this paper we present a global approach for the automatic inspection of defects in randomly textured surfaces which arise in sandpaper, castings, leather, and many industrial materials. The proposed method does not rely on local features of textures. It is based on a global image reconstruction scheme using the Fourier transform (FT). Since a statistical texture has the surface of random pattern, the spread of frequency components in the power spectrum space is isotropic and forms the shape approximate to a circle. By finding an adequate radius in the spectrum space, and setting the frequency components outside the selected circle to zero, we can remove the periodic, repetitive patterns of any statistical textures using the inverse FT. In the restored image, the homogeneous region in the original image will have an approximately uniform gray level, and yet the defective region will be distinctly preserved. This converts the difficult defect detection in textured images into a simple thresholding problem in nontextured images. The experimental results from a variety of real statistical textures have shown the efficacy of the proposed method. <s> BIB027 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> This article dealth with a complex problem of textile quality control by the means of computer vision using advanced digital signal and image processing tools. An investigation into a computer vision solution to quality inspection of textiles suggested a texture analysis approach. Since each texture analysis method presents a different potential for analysis of textured textile images, a large number of standard approaches from this category was examined. By considering their advantages and potential for successful flaw detection performance in homogeneous and jacquard textiles, six standard texture analysis techniques were identified for further investigation. A comprehensive examination and evaluation of these chosen techniques resulted. Normalised cross-correlation and SCLC approaches were identified as suitable candidates for real-time flaw detection in textiles with homogeneous structure.Inspection of jacquard textiles, however, presents a problem of greater complexity, the solution to which has not as yet been documented in the literature. To address this problem, a need for the application of a comprehensive cojoint spatial-spatial frequency approach was identified. A Gabor filter approach was chosen as a suitable representative of this class of techniques. This research then successfully applied optimised 2-D Gabor filters to the textile flaw detection problem and provided a further support of their suitability for this task.A novel optimised 2-D Gabor algorithm presented in this study is an automatic solution which is adaptable to detect a large variety of textile flaw types, both structural and tonal. <s> BIB028 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> A new eigenfilter-based novelty detection approach to find abnormalities in random textures is presented. The proposed algorithm reconstructs a given texture twice using a subset of its own eigenfilter bank and a subset of a reference (template) eigenfilter bank, and measures the reconstruction error as the level of novelty. We then present an improved reconstruction generated by structurally matched eigenfilters through rotation, negation, and mirroring. We apply the method to the detection of defects in textured ceramic tiles. The method is over 90% accurate, and is fast and amenable to implementation on a production line. <s> BIB029 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> A 2D Gabor filter can be realized as a sinusoidal plane wave of some frequency and orientation within a two dimensional Gaussian envelope. Its spatial extent, frequency and orientation preferences as well as bandwidths are easily controlled by the parameters used in generating the filters. However, there is an "uncertainty relation" associated with linear filters which limits the resolution simultaneously attainable in space and frequency. Daugman (1985) has determined that 2D Gabor filters are members of a class of functions achieving optimal joint resolution in the 2D space and 2D frequency domains. They have also been found to be a good model for two dimensional receptive fields of simple cells in the striate cortex (Jones 1985; Jones et al. 1985). The characteristic of optimal joint resolution in both space and frequency suggests that these filters are appropriate operators for tasks requiring simultaneous measurement in these domains. Texture discrimination is such a task. Computer application of a set of Gabor filters to a variety of textures found to be preattentively discriminable produces results in which differently textured regions are distinguished by first-order differences in the values measured by the filters. This ability to reduce the statistical complexity distinguishing differently textured region as well as the sensitivity of these filters to certain types of local features suggest that Gabor functions can act as detectors of certain "texton" types. The performance of the computer models suggests that cortical neurons with Gabor like receptive fields may be involved in preattentive texture discrimination. <s> BIB030 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Over the last few years research has been oriented toward developing a machine vision system for locating and identifying, automatically, defects on rails. Rail defects exhibit different properties and are divided into various categories related to the type and position of flaws on the rail. Several kinds of interrelated factors cause rail defects such as type of rail, construction conditions, and speed and/or frequency of trains using the rail. The aim of this paper is to present an experimental comparison among three filtering approaches, based on texture analysis of rail surfaces, to detect the presence/absence of a particular class of surface detects: corrugation. <s> BIB031 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Wavelet transform is able to characterize the fabric texture at multiscale and multiorientation, which provides a promising way to the classification of fabric defects. For the objective of minimum error rate in the defect classification, this paper compares six wavelet transform-based classification methods, using different discriminative training approaches to the design of the feature extractor and classifier. These six classification methods are: methods of using an Euclidean distance classifier and a neural network classifier trained by maximum likelihood method and backpropagation algorithm, respectively; methods of using an Euclidean distance classifier and a neural network classifier trained by minimum classification error method, respectively; method of using a linear transformation matrix-based feature extractor and an Euclidean distance classifier, designed by discriminative feature extraction (DFE) method; method of using an adaptive wavelet-based feature extractor and an Euclidean distance classifier, designed by the DFE method. These six approaches have been evaluated on the classification of 466 defect samples containing eight classes of fabric defects, and 434 nondefect samples. The DFE training approach using adaptive wavelet has been shown to outperform the other approaches, where 95.8% classification accuracy was achieved. <s> BIB032 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> AbstractThe present paper presents a Gabor filtering approach for the automatic inspection of defects in coloured texture surfaces. It can simultaneously measure both chromatic and textural anomalies in an image. Two chromatic features derived from the CIE–L*a*b* colour space are used to form a complex number for colour pixel representation. The proposed method is based on the energy response from the convolution of a Gabor filter with the colour image characterized by two chromatic features in the form of a complex number. The Gabor filtering process converts the difficult defect detection in a coloured texture image into simple threshold segmentation in the filtered image. Experimental results from a number of coloured texture surfaces such as textile fabric, wood and tile have shown the effectiveness of the proposed method. <s> BIB033 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Several continuous manufacturing processes use stochastic texture images for quality control and monitoring. Large amounts of pictorial data are acquired, providing both important information about the materials produced and about the manufacturing processes involved. However, it is often difficult to measure objectively the similarity among such images, or to discriminate between texture images of materials with distinct properties. The degree of discrimination required by industrial processes sometimes goes beyond the limits of human visual perception. This work presents a new method for multi-resolution stochastic texture analysis, interpretation and discrimination based on the wavelet transform. A multi-resolution distance measure for stochastic textures is proposed, and applications of our method in the non-woven textiles industry are reported. The conclusions include ideas for future work. <s> BIB034 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> The wavelet transform has been widely used for defect detection and classification in fabric images. The detection and classification performance of the wavelet transform approach is closely related to the selection of the wavelet. Instead of predetermining a wavelet, a method of designing a wavelet adapting to the detection or classification of the fabric defects has been developed. For further improvement of the performance, this paper extends the adaptive wavelet-based methodology from the use of a single adaptive wavelet to multiple adaptive wavelets. For each class of fabric defect, a defect-specific adaptive wavelet was designed to enhance the defect region at one channel of the wavelet transform, where the defect region can be detected by using a simple threshold classifier. Corresponding to the multiple defect-specific adaptive wavelets, the multiscale edge responses to defect regions have been shown to be more efficient in characterising the defects, which leads to a new approach to the classification of defects. In comparison with the single adaptive wavelet approach, the use of multiple adaptive wavelets yields better performance on defect detection and classification, especially for defects that are poorly detected by the single adaptive wavelet approach. The proposed method using multiple adaptive wavelets has been evaluated on the inspection of 56 images containing eight classes of fabric defects, and 64 images without defects, where 98.2% detection rate and 1.5% false alarm rate were achieved in defect detection, and 97.5% classification accuracy was achieved in defect classification. <s> BIB035 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> We present an effective approach based on wavelet transform (WT) to detect defects on images with high frequency texture background. The original image is decomposed at various levels by WT. Then, by selecting an appropriate level at which the approximation sub-image is reconstructed, textures on the background are effectively removed. Thus, the difficult texture defect detection problem can be settled by non-texture techniques. An adaptive level-selecting scheme is presented by analyzing the co-occurrence matrices (COM) of the approximation sub-images. Experiments are done to detect the stains and broken points on texture surfaces. Comparisons with frequency domain low and high pass filters show that our method is much more effective. <s> BIB036 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> This paper presents a wavelet characteristic based approach for the automated visual inspection of ripple defects in the surface barrier layer (SBL) chips of ceramic capacitors. Difficulties exist in automatically inspecting ripple defects because of their semi-opaque and unstructured appearances, the gradual changes of their intensity levels, and the low intensity contrast between their surfaces and the rough exterior of a SBL chip. To overcome these difficulties, we first utilize wavelet transform to decompose an image and use wavelet characteristics as texture features to describe surface texture properties. Then, we apply multivariate statistics of Hotelling T^2, Mahalanobis distance D^2, and Chi-square X^2, respectively, to integrate the multiple texture features and judge the existence of defects. Finally, we compare the defect detection performance of the three wavelet-based multivariate statistical models. Experimental results show that the proposed approach (Hotelling T^2) achieves a 93.75% probability of accurately detecting the existence of ripple defects and an approximate 90% probability of correctly segmenting their regions. <s> BIB037 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> This paper studies the application of advanced computer image processing techniques for solving the problem of automated defect detection for textile fabrics. A new defect detection scheme is proposed, which consists of an odd symmetric real-valued Gabor filter, an even symmetric real-valued Gabor filter and one smoothing filter. In developing the scheme, the Gabor filters are designed on the basis of the texture features extracted optimally from a non-defective fabric image by using a Gabor wavelet network (GWN). The performance of the proposed defect detection scheme is evaluated off-line by using a set of fabric images taken from a database consisting of a wide variety of homogeneous fabric images. The results exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed scheme. To evaluate the performance of the proposed defect detection scheme further, real-time tests are conducted by using a prototyped automated defect detection system. The experimental results obtained further confirm the efficiency, effectiveness and robustness of the proposed detection scheme. <s> BIB038 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Two methods are proposed in this paper to inspect printed fabrics. One method is to apply a genetic algorithm to select parameters of optimal Gabor filter. Optimal Gabor filter can reduce the noise information of printed fabrics, which can achieve defect detection of printed fabrics. The other is in utilizing distance matching function to determine the unit of printed fabrics. Extracting features on a moving unit of printed fabrics can realize defect segmentation of printed fabrics. Two approaches of defect detection have their own advantages. Detecting method with Gabor filter using genetic algorithm has perfect detection results of random printed fabrics, the other method based on statistical rule can receive better defect detection results of regular printed fabrics. Both methods can be realized in practice and detection time of proposed methods can occupy little in total detection time. <s> BIB039 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> In order to improve the accuracy of detection of defects in banknote sorting, a new algorithm is proposed to detect cracks and scratches on banknote images. The quaternion wavelet transform and the least squares method are used for the banknote image registration. The features of the defects that are robust to gray intensity changes are constructed using edge information captured by the Kirsch operator. The banknote image is divided into several subzones of fixed size. The level of defect of the banknote image is estimated based on the defective features of each sub-zone. The experimental results show that the proposed algorithm is robust even with low quality banknote images and can obtain a high recognition rate and high stability. The proposed algorithm has already been used in a practical banknote sorting system. The quaternion wavelet transform is applied to banknote image registration.Defect banknote features are extracted by the proposed edge intensity.New banknote defect detection algorithm based on our QWT is proposed. <s> BIB040 </s> Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY <s> Filter-Based Approaches <s> Glass bottles are widely used as containers in the food and beverage industry, especially for beer and carbonated beverages. As the key part of a glass bottle, the bottle bottom and its quality are closely related to product safety. Therefore, the bottle bottom must be inspected before the bottle is used for packaging. In this paper, an apparatus based on machine vision is designed for real-time bottle bottom inspection, and a framework for the defect detection mainly using saliency detection and template matching is presented. Following a brief description of the apparatus, our emphasis is on the image analysis. First, we locate the bottom by combining Hough circle detection with the size prior, and we divide the region of interest into three measurement regions: central panel region, annular panel region, and annular texture region. Then, a saliency detection method is proposed for finding defective areas inside the central panel region. A multiscale filtering method is adopted to search for defects in the annular panel region. For the annular texture region, we combine template matching with multiscale filtering to detect defects. Finally, the defect detection results of the three measurement regions are fused to distinguish the quality of the tested bottle bottom. The proposed defect detection framework is evaluated on bottle bottom images acquired by our designed apparatus. The experimental results demonstrate that the proposed methods achieve the best performance in comparison with many conventional methods. <s> BIB041
Images can be described by detected features, such as edges, textures and regions (Figure 3 ). Filtering these features is one of the earliest attempts in image processing, especially for the extraction of the edge details. It is also a low-level process and the edges can be interpreted as spatial impulsive intensity changes of the image BIB001 . To extract edges from images, it is possible to use several filters and algorithms in the spatial domain, such as Sobel, Robert, Canny, Deriche, Laws and Laplacian filters. Neubauer BIB007 introduced a method with three 5 × 5 finite impulse response (FIR) filters as first-order statistics, and performed defect segmentation with TP = 98.3% and TN = 90.6%. Unser and Ade BIB004 BIB003 , and then Monadjemi et al.  BIB029 , used texture independent ensemble of macro windows called eigenfilters as defect detectors. Eigenfilters are considered highly sensitive to local distortion and noise; it makes them less suitable for online fabric inspections. However, their low complexity and ability to incorporate various time-and frequency-domain constraints easily compared to other methods, have made eigenfilters very commonly used as general approach. In most cases, operating in the spatial domain involves noise and complications to find a direct kernel. Therefore, transforming the images into the frequency domain with Fourier Transformation (FT) gives the leverage to easily filter the noise and process the image, as described in BIB015 . The basic logics are to transform the image into Fourier domain, filter and then re-transform it into the spatial domain. The differences between the original and processed images can be considered as potential defects, based on the applied function in the transformation BIB027 . Chan and Pang  applied a central spatial frequency spectrum, based on the idea that defects usually occur in horizontal and vertical directions. However, these filters suffer the assumption that textures are periodic. D'Astous and Jernigan  used peak and power distribution features to discriminate textures. Optical Fourier transform (OFT) has been used in several applications performed on fabrics, such as in BIB009 BIB040 performed the quaternion wavelet transform on 10,000 banknote images and achieved 97% defect detection success rate and 0.35% false alarm. The Fourier transformation depends on the entire image because of its coefficients. This property makes it unable to localize defects in the spatial domain. The most common solution for this problem is to apply a windowed FT for spatial dependency and, if the window function is Gaussian, it results in the well-known Gabor transform. The Gabor transform (GT) attempts the optimal joint localization in spatial and spatial-frequency domains BIB002 . There are two types of approaches to this method. The first is when several filters have been stored in predetermined frequencies and orientations to cover all possibly occurring frequencies in the image and calculate their correlation BIB038 . However, this approach can be computationally intensive to achieve high recognition quality. The second approach concerns the implementation of the optimal filters that correlate with the desired recognition area in parameters, but achieving the optimal settings is hard and crucial BIB024 . Turner BIB030 and Clark et al. BIB005 first proposed the use of Gabor filters (GF) in texture analysis. In the past decades, several applications were published about Gabor filters  BIB017 BIB024 BIB010 BIB018 BIB019 BIB020 BIB028 BIB033 . Kumar and Pang BIB019 used only real Gabor functions for fabric defect detection, and then, in BIB017 , they used similar features on plain and twill fabrics in three schemes with no explicit results in the first scheme, but with 100% accuracy in the second and third schemes. They also investigated the imaginary part of the Gabor functions as an edge detector. In BIB024 BIB021 , Bodnarova et al. applied a Fisher cost function to select a subset of Gabor functions to perform flaw detection on textiles and achieved 82.86% accuracy with the proposed optimal two-dimensional GF. Escofet et al. BIB010 performed a multi-scale and multiresolution Gabor filtering in a novelty detection framework. Among the GT methods, Mak and Peng BIB038 achieved the best detection results on a fair number and quality of samples. They achieved 96.2% success rate with a Gabor wavelet network to extract optimal texture features from a defect-free image and then 97.1% with an only real Gabor filter for defect detection. Recently, Kang et al. BIB039 proposed two approaches: an optimized Gabor filter and a distance-matching-based method called regular band. They achieved a 71.4% detection rate with 0% false alarm with Gabor filtering and a 93.1% detection rate with 4.9% false positives on 85 sample images from TILDA database with regular band. Hu  established an elliptical Gabor filter tuned by a simulated annealing algorithm, but no detailed defect detection results were published. With similar properties to the Gabor transform, Wavelet Transform (WT) representations have also been used as defect detectors BIB022 BIB011 BIB016 BIB013 BIB023  BIB025 BIB031 BIB034 BIB035  . WTs are based on small waves of varying frequency and limited duration called wavelets and provide local information from horizontal, vertical and diagonal directions on any input image BIB006 . Several approaches managed to achieve 98-100% success rate for defect detection with the Fuzzy Wavelet Analysis BIB008 , multiscale wavelet method BIB012 , WT image restoration schemes BIB027 BIB023 and adaptive level-selecting scheme to analyze co-occurrence matrices BIB036 . However, the reliability of these methods is questionable due to a limited dataset of samples used during the tests. In BIB013 BIB014 , Sari-Sarraf and Goddard performed discrete WTs and edge fusions to emphasize the defects from the background on fabric images. The procedure achieved an 89% detection success rate over 3700 images of fabrics, containing 26 distinct kinds of defects. Yang et al. BIB026 developed an adaptive wavelet-based feature extractor with a Euclidean distance-based detector for fabric images, which achieved 97.5% with a defect-database (480 defect-free and 480 defective samples), and 93.3% without a defect-database (780 defect-free and 180 defective samples). Later, in BIB032 , they outperformed five other WT-based methods with their new Discriminative Feature Extraction (DFE) method, reaching 95.8% classification accuracy. In BIB037 , Lin used one-level Harr wavelet transform to detect ripple defects on chips. Recently, Zhou et al. BIB041 proposed two new saliency detection method-region growing geodesic saliency (RGGS) and region growing Euclidean saliency (RGES)-and template matching with multiscale mean filtering. Their template matching method achieved an 88.83% detection rate, while the RGES method performed with an accuracy of 75.95%.