A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> Abstract A unified approach to how colour, texture and shape can be encoded in a single spatio-chromatic feature space is developed. In particular, it is shown how such features can be extracted using multi-scaled filtering and correlation methods which capture the variations of colour over space in ways which encode important image features not extracted by techniques which separate colour, texture and shape into separate channels. Finally, it is shown how such spatio-chromatic features can be used in difficult classification problems. <s> BIB001 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> An important area of automatic industrial inspection that has been largely overlooked by the recent wave of research in machine vision applications is the detection of defects in textured surfaces. In this paper, we present different algorithms for the detection of surface abnormalities both in the chromatic and structural properties of random textures. We present very promising results on the detection of cracks, blobs, and chromatic defects in ceramic and granite tiles. <s> BIB002 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> We present an unsupervised segmentation algorithm which uses Markov random field models for color textures. These models characterize a texture in terms of spatial interaction within each color plane and interaction between different color planes. The models are used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of agglomerative clustering is a stepwise optimal merging process that at each iteration maximizes a global performance functional based on the conditional pseudolikelihood of the image. A test for stopping the clustering is applied based on rapid changes in the pseudolikelihood. We provide experimental results that illustrate the advantages of using color texture models and that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation. > <s> BIB003 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> Abstract This paper describes a new algorithm for combining color and texture information for the segmentation of color images. The algorithm uses maximum likelihood classification combined with a certainty based fusion criterion. The algorithm was validated using mosaics of real color textures. It was also tested on real outdoor color scenes and aerial images. This algorithm is part of a more complex system, which is currently being designed to assist an operator in updating an old map of an area using aerial images. <s> BIB004 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> We introduce a representation for color texture using unichrome and opponent features computed from Gabor filter outputs. The unichrome features are computed from the spectral bands independently while the opponent features combine information across different spectral bands at different scales. Opponent features are motivated by color opponent mechanisms in human vision. We present a method for efficiently implementing these filters, which is of particular interest for processing the additional information present in color images. Using a data base of 2560 image regions, we show that the multiscale approach using opponent features provides better recognition accuracy than other approaches. <s> BIB005 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> A new class of multiscale symmetry features provides a useful high-level representation for color texture. These symmetry features are defined within and between the bands of a color image using complex moments computed from the output of a bank of orientation and scale selective filters. We show that these features not only represent symmetry information but are also invariant to rotation, scale, and illumination conditions. The features computed between color bands are motivated by opponent process mechanisms in human vision. Experimental results are provided to show the performance of this set of features for texture classification and retrieval. <s> BIB006 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> Multispectral extensions to the traditional gray level simultaneous autoregressive (SAR) and Markov random field (MRF) models are considered. Furthermore, a new image model is proposed, the pseudo-Markov model, which retains the characteristics of the multispectral Markov model, yet admits to a simplified parameter estimation method. These models are well-suited to analysis and modeling of color images. For each model considered, procedures are developed for parameter estimation and image synthesis. Experimental results, based on known image models and natural texture samples, substantiate the validity of thee results. <s> BIB007 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> This paper describes an approach to perceptual segmentation of color image textures. A multiscale representation of the texture image, generated by a multiband smoothing algorithm based on human psychophysical measurements of color appearance is used as the input. Initial segmentation is achieved by applying a clustering algorithm to the image at the coarsest level of smoothing. The segmented clusters are then restructured in order to isolate core clusters, i.e., patches in which the pixels are definitely associated with the same region. The image pixels representing the core clusters are used to form 3D color histograms which are then used for probabilistic assignment of all other pixels to the core clusters to form larger clusters and categorise the rest of the image. The process of setting up color histograms and probabilistic reassignment of the pixels to the clusters is then propagated through finer levels of smoothing until a full segmentation is achieved at the highest level of resolution. <s> BIB008 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> Methods for extracting features and classifying textures in high resolution colour images are presented. The proposed features are directional texture features obtained from the convolution of the Walsh-Hadamard transform with different orientations of texture patches from high resolution images, as well as simple chromatic features that correspond to hue and saturation in the HLS colour space. We compare the performance of these new features against Gabor transform features combined with HLS and Lab colour space features. Multiple classifiers are employed to combine both textural and chromatic features for better classification performance. We demonstrate a considerable reduction in computational costs, whilst maintaining close accuracy. <s> BIB009 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> Herein, we propose a novel multilayer Markov random field (MRF) image segmentation model which aims at combining color and texture features: each feature is associated to a so called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The model is quite generic and isn't restricted to a particular texture feature. Herein we will test the algorithm using Gabor and MRSAR texture features. Furthermore, the algorithm automatically estimates the number of classes at each layer (there can be different classes at different layers) and the associated model parameters. <s> BIB010 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> We present novel simple appearance and shape models that we call epitomes. The epitome of an image is its miniature, condensed version containing the essence of the textural and shape properties of the image. As opposed to previously used simple image models, such as templates or basis functions, the size of the epitome is considerably smaller than the size of the image or object it represents, but the epitome still contains most constitutive elements needed to reconstruct the image. A collection of images often shares an epitome, e.g., when images are a few consecutive frames from a video sequence, or when they are photographs of similar objects. A particular image in a collection is defined by its epitome and a smooth mapping from the epitome to the image pixels. When the epitomic representation is used within a hierarchical generative model, appropriate inference algorithms can be derived to extract the epitome from a single image or a collection of images and at the same time perform various inference tasks, such as image segmentation, motion estimation, object removal and super-resolution. <s> BIB011 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> Luminance, colour, and/or texture features may be used, either alone or in combination, for segmentation. In this paper luminance and colour classes are described using the corresponding empirical probability distributions. For texture analysis and characterisation a multichannel scale/orientation decomposition is performed using wavelet frame analysis. Knowing only the number of the different classes of the image, regions of homogeneous patterns are identified. On these regions the features characterising and describing the different classes are estimated. Two labelling algorithms are proposed. The first, a deterministic relaxation algorithm using a quadratic distance measure, yields the labelling of pixels to the different colour–texture classes. The second is a new Multi-label Fast Marching algorithm utilising a level set boundary determination. <s> BIB012 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> This paper describes a color texture-based image segmentation system. The color texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The general color content characterized by ratios of sample color means is also used. The image is segmented into regions of uniform color texture using an unsupervised histogram clustering approach that utilizes the combination of MSAR and color features. The performance of the system is tested on two databases containing synthetic mosaics of natural textures and natural scenes, respectively <s> BIB013 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> Abstract Integrative Co-occurrence matrices are introduced as novel features for color texture classification. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information profit of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classification experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classification results up to 20% and 32% for the first and second baseline, respectively. <s> BIB014 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> This paper demonstrates a new application of computer vision to digital libraries — the use of texture forannotation, the description of content. Vision-based annotation assists the user in attaching descriptions to large sets of images and video. If a user labels a piece of an image aswater, a texture model can be used to propagate this label to other “visually similar” regions. However, a serious problem is that no single model has been found that is good enough to match reliably human perception of similarity in pictures. Rather than using one model, the system described here knows several texture models, and is equipped with the ability to choose the one that “best explains” the regions selected by the user for annotating. If none of these models suffices, then it creates new explanations by combining models. Examples of annotations propagated by the system on natural scenes are given. The system provides an average gain of four to one in label prediction for a set of 98 images. <s> BIB015 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <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> BIB016 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <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> BIB017 </s> A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques <s> Colour Texture Analysis <s> We present an approach to detecting and localizing defects in random color textures which requires only a few defect free samples for unsupervised training. It is assumed that each image is generated by a superposition of various-size image patches with added variations at each pixel position. These image patches and their corresponding variances are referred to here as textural exemplars or texems. Mixture models are applied to obtain the texems using multiscale analysis to reduce the computational costs. Novelty detection on color texture surfaces is performed by examining the same-source similarity based on the data likelihood in multiscale, followed by logical processes to combine the defect candidates to localize defects. The proposed method is compared against a Gabor filter bank-based novelty detection method. Also, we compare different texem generalization schemes for defect detection in terms of accuracy and efficiency. <s> BIB018
Due to the increasing computational power and availability of colour cameras, there are rising demands to use colour when necessary. There has been a limited but increasing amount of work on colour texture analysis applied to surface inspection recently (cf. Table 1) . Most colour texture analysis techniques are borrowed from methods designed for graylevel images, such as co-occurrence matrices and LBP. This extension of graylevel texture analysis techniques to deal with colour images usually takes one of the following forms: 1. Processing each channel individually by directly applying graylevel based methods BIB001    BIB017 : The channels are assumed independent to each other and only the spatial interactions are taken into account. 2. Decomposing image into luminance and chromatic channels BIB015  BIB004 BIB009 BIB012 : Transforming the colour space so that texture features are extracted from the luminance channel and chromatic features from the chromatic channels, each in a specific manner. The selection of the colour space is usually application dependent. 3. Combining spatial interaction within each channel and interaction between spectral channels  BIB002 BIB003 BIB005 BIB006 BIB007 BIB008 BIB010 BIB013 BIB014 : The graylevel texture analysis techniques are applied in each channel, while the pixel interactions between different channels are also taken into account. Also, some works perform global colour clustering analysis, followed by spatial analysis in each individual stack. Techniques independent of graylevel methods have also been attempted: 4. Using fully three dimensional models to analyse colour textures BIB011 BIB018 : The spatial and spectral interactions are simultaneously handled. The main difficulties arise in effectively representing, generalising, and discriminating three dimensional data. Caelli and Reye BIB001 processed colour images in RGB channels using multiscale isotropic filtering. Features from each channel were then extracted and later combined for classification. In  , the author used the KL transform to decorrelate the RGB channels into orthogonal eigenchannels. A recursive MRF model was performed in individual channels for texture segmentation. Later in  , Haindl and Havlicek used a similar approach for colour texture synthesis. Mäenpää et al.  measured colour percentiles based on the accumulated histogram in each RGB channel as chromatic features, and co-occurrence matrices and LBP features as textural features to inspect wood surfaces. Lin BIB017 extracted wavelet features from each RGB channel separately to detect ripple-like defects in surface barrier layer chips. Several works transform the RGB colour space to other colour spaces to perform texture analysis so that chromatic channels are separated from the luminance channel, e.g. BIB015  BIB004 BIB009 BIB012 . In  , Paschos et al. projected the colour images into the xyY colour space. The two chromaticity coordinates (x,y) were combined into one, which provided the chromatic features. Texture features were extracted from the Y channel. Dubuisson-Jolly and Gupta BIB004 used a multi-resolution simultaneous auto-regressive model to compute the texture features. Very simple colour features were selected from the Y uv colour space. Similarly, Monadjemi et al. BIB009 used hue-like colour features, and Hadamard and Gabor transform texture features to classify outdoor scenes. Liapis et al. BIB012 transformed colour images into the L * a * b * colour space in which discrete wavelet frame transform was performed in the L channel. Local histograms in a and b channels were used as chromatic features. Recently, Tsai et al. BIB016 also transformed colour images into the L * a * b * space, from which two chromatic representations were derived for each pixel colour, hue and chroma (colourfulness). Gabor filtering was then performed in these two channels. The authors argued that processing images in these two chromatic channels only could be resilient to illumination changes. They assumed that defects were chromatically differentiable. However, a large set of defects occur due to intensity irregularities. For example, changes in gray shade will not introduce differences in hue and chroma. The importance of extracting correlation between the channels for colour texture analysis has been addressed by several authors. One of the earliest attempts was reported in  . In BIB003 , Panjwani and Healey devised a MRF model to encode the spatial interaction within colour channels and between colour channels. A similar idea was explored in BIB010 for unsupervised colour image segmentation. In BIB005 , Jain and Healey used Gabor filters to obtain texture features in each channel and opponent features that capture the spatial correlation between channels. Thai and Healey BIB006 applied multiscale opponent features computed from Gabor filter responses to model intra-channel and inter-channel interactions. In BIB008 , Mirmehdi and Petrou perceptually smoothed the colour image textures in a multiresolution sense before segmentation. Core clusters were then obtained from the coarsest level and initial probabilities were assigned to all the pixels for all clusters. A probabilistic reassignment was then propagated through finer levels until full segmentation was achieved. Simultaneous auto-regressive models and co-occurrence matrices have also been used to extract the spatial relationship within and between RGB channels BIB007 BIB013 BIB014 . In BIB002 , the authors performed colour clustering, followed by binarised spatial pixel distribution analysis, to identify textural defects in colour ceramic tile images. The colour clustering and binarisation in the spatial domain partially takes into account both spatial and spectral interactions. There is relatively limited effort to develop fully 3D models to represent colour textures. The 3D data space is usually factorised using one of the approaches mentioned above, then the data is modelled and analysed using lower dimensional methods. However, such methods inevitably suffer from some loss of spectral information, as the colour image data space can only be approximately decorrelated. The epitome BIB011 and texem BIB018 models provide compact 3D representations of colour textures. The image is assumed to be a collection of primitives relying on raw pixel values in image patches. The neighbourhood of a central pixel in a patch are assumed statistically conditionally independent. In epitome, a hidden mapping guides the relationship between the epitome and the original image; in texem, specific mapping is provided by using multiple smaller epitomic representations. These compact representation methods inherently capture the spatial and spectral interactions simultaneously. However, full 3D methods are usually computationally expensive. Special hardware and software are necessary in order to adapt to real time performance. Visual inspection using colour texture analysis is still largely under-developed in the literature and only a limited number of works have been reported so far. However, the demand for colour visual inspection is rising.