A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB001 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <s> Abstract We measured the spatial frequency contrast sensitivity of cells in the primate striate cortex at two different eccentricities to provide quantitative statistics from a large population of cells. Distributions of the peak frequencies and bandwidths are presented and examined in relationship to (a) each other, (b) absolute contrast sensitivity, (c) orientation tuning, (d) retinal eccentricity, and (e) cell type. Simple and complex cells are examined in relationship to linear/nonlinear (that is, X/Y) properties; a procedure is described which provides a simple, reliable and quantitative method for classifying and describing striate cells. Among other things, it is shown that (a) many striate cells have quite narrow spatial bandwidths and (b) at a given retinal eccentricity, the distribution of peak frequency covers a wide range of frequencies; these findings support the basic multiple channel notion. The orientation tuning and spatial frequency tuning which occurs at the level of striate cortex (in a positively correlated fashion) suggests that the cells might best be considered as two-dimensional spatial filters. <s> BIB002 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB003 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <s> A texture segmentation algorithm inspired by the multichannel filtering theory for visual information processing in the early stages of the human visual system is presented. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain. A systematic filter selection scheme based on reconstruction of the input image from the filtered images is proposed. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of energy in a window around each pixel. An unsupervised square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial adjacency information in the clustering process is proposed. Experiments on images with natural textures as well as artificial textures with identical second and third-order statistics are reported. The algorithm appears to perform as predicted by preattentive texture discrimination by a human. > <s> BIB004 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <s> This paper describes a new approach to the characterization of texture properties at multiple scales using the wavelet transform. The analysis uses an overcomplete wavelet decomposition, which yields a description that is translation invariant. It is shown that this representation constitutes a tight frame of l/sub 2/ and that it has a fast iterative algorithm. A texture is characterized by a set of channel variances estimated at the output of the corresponding filter bank. Classification experiments with l/sub 2/ Brodatz textures indicate that the discrete wavelet frame (DWF) approach is superior to a standard (critically sampled) wavelet transform feature extraction. These results also suggest that this approach should perform better than most traditional single resolution techniques (co-occurrences, local linear transform, and the like). A detailed comparison of the classification performance of various orthogonal and biorthogonal wavelet transforms is also provided. Finally, the DWF feature extraction technique is incorporated into a simple multicomponent texture segmentation algorithm, and some illustrative examples are presented. > <s> BIB005 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB006 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB007 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB008 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB009 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB010 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB011 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB012 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB013 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB014 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB015 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB016 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB017 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB018 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB019 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <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> BIB020 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Joint spatial/spatial-frequency methods <s> Twenty five years after the seminal work of Jean Morlet, the wavelet transform, multiresolution analysis, and other space-frequency or space-scale approaches are considered standard tools by researchers in image processing. Many applications that point out the interest of these techniques have been proposed. We review the recent published work dealing with industrial applications of the wavelet and, more generally speaking, multiresolution analysis. We present more than 190 recent papers. <s> BIB021
Since the Fourier coefficients are depending on the entire image, the Fourier transform is not able to localise the defective regions in the spatial domain. The classical way of introducing spatial dependency into Fourier analysis is through the windowed Fourier transform. If the window function is Gaussian, the windowed Fourier transform becomes the well-known Gabor transform, which can arguably achieve optimal localisation in the spatial and frequency domains BIB001 . Psychophysiological studies, such as BIB002 , have suggested that the human brain performs multi-channel, frequency and orientation analysis on the visual image. These findings have strongly motivated the use of Gabor analysis, along with other multiscale techniques. Turner BIB017 and Bovik et al. BIB003 first proposed the use of Gabor filters in texture analysis. Jain and Farrokhnia BIB004 used it in segmentation and classification of textures with dyadic coverage of the radial spatial frequency range. The Gabor filter bank has been extensively studied in visual inspection, e.g. BIB006 BIB008 BIB009 BIB010 BIB014 BIB011 BIB016  BIB018 . Kumar and Pang BIB009 performed fabric defect detection using only real Gabor functions. Later in BIB011 , the same authors used a class of self-similar Gabor functions to classify fabric defects. They also investigated defect detection using only imaginary Gabor functions as an edge detector. For computational efficiency, the fabric samples were analysed using horizontally and vertically projected one-dimensional profiles. In BIB014 , Bodnarova et al. applied a Fisher cost function to select a subset of Gabor functions based on the mean and standard deviation of the template (defect-free) feature images to perform textile flaw detection. The filtering responses of those selected Gabor functions were supposed to have compact distributions. Defects were localised by thresholding the filtering responses from an unseen image sample based on the mean and standard deviation of template filtering responses. Tsai and Wu BIB010 also performed Gabor filter selection so that the filter response energy of the normal texture, assumed to be homogeneous, was close to zero. Wiltschi et al. BIB008 performed automatic scale selection to preserve channels with maximum energy and directional information. In BIB006 , Escofet et al. performed multiscale Gabor filtering in a novelty detection framework. Defect candidates across different scales and orientations were fused together using logical processes. Havig similar properties to the Gabor transform, wavelet transform representations have also been widely used for defect detection, e.g. defect detection and localisation BIB007   BIB012 BIB013  BIB015  BIB019 BIB020  . Wavelet analysis uses approximating functions that are localised in both spatial and spatial-frequency domain. The input signal is considered as the weighted sum of overlapping wavelet functions, scaled and shifted. These functions are generated from a basic wavelet (or mother wavelet) by dilation and translation. The spatial resolution of wavelet transform is adapted to its frequency content, unlike in Gabor transform the spatial resolution is constant. In  , Sari-Sarraf and Goddard performed discrete wavelet transforms on fabric images. The detailed images were fused together to produce a feature map in which the normal texture regions, assumed to be homogeneous, had small values. The defects were segmented by thresholds learnt from training templates. The key process was to attenuate the normal regions, and accentuate the defective regions, based on the assumption that normal texture was regular and homogeneous, and defects were those that broke the local homogeneity. Scharcanski BIB019 also used the discrete wavelet transform to classify stochastic textile textures. Rather using fixed dyadic scales, Kim et al.  employed a learning process to choose the wavelet scales for maximising the detectability of fabric defects. Latif-Amet et al. BIB012 extracted co-occurrence and MRF-based features from wavelet transform coefficients for fabric defect detection. Graylevel difference-based features from subbands of the wavelet transform were also applied in classifying fabric defects. Recently, Yang et al. BIB020 used adaptive wavelets resulting in fewer scales compared with the standard wavelet transform. The wavelet functions were adaptively selected based on an objective function measuring the ratio of average energies between defective regions and defect-free regions. The method achieved better performance than the standard wavelet transform, but needed supervised training. Wavelet frames BIB005 and image reconstruction techniques using wavelets were also used for defect detection BIB015 BIB013 . Recently, in [71] Lin used the onelevel Harr wavelet transform to decompose surface barrier layer chip images and extracted wavelet features from normal samples and testing samples were statistically compared based on Hotelling, Mahalanobis, and Chi-square distances to detect ripple defects. The experimental results showed that Hotelling and Mahalanobis measures were superior in detecting those ripple defects than Chi-square testing. Very recently, Truchetet and Laligant BIB021 gave a very detailed review on wavelet analysis in industrial applications.