Fabric Texture Analysis and Weave Pattern Recognition by Intelligent Processing

Coimbatore is a major city in the Indian state of Tamil Nadu located on the banks of the Noyyal River surrounded by the Western Ghats. It is one of the biggest centers of textile manufacturing in India. A fast-growing metropolitan area city, it is home to over 25,000 textile and manufacturing companies and has spawned many new centers of textiles around it. Textile fabric automation and manufacturing has been of great concern over the past decade. This is a remarkable task because of the accidental changes of fabric material properties. Due to the increasing demand of consumers for high-quality textile products, an automatic and objective evaluation of the fabric texture appearance is necessary with respect to geometric structure characteristics, surface, and mechanical properties. The precise measurement of the fabric texture parameters, such as weave structure and yarn counts find wide applications in the textile industry, virtual environments, e-commerce, and robotic telemanipulation. The weave pattern and the yarn count are analyzed and determined for computer simulated sample images and also for the scanned real fabric images. 2-D integral projections are used to identify the accurate structure of the woven fabric and to determine the yarn count. They are used for segmenting the crossed areas of yarns and also to detect the defects like crossed area due to the random distribution of yarns. Fuzzy C-Means Clustering (FCM) is applied to multiscale texture features based on the Grey Level Co-Occurrence Matrix (GLCM) to classify the different crossed-area states. Linear Discriminant Analysis (LDA) is used to improve the classifier performance

Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer

The clinical diagnostic evaluation of optic neuropathies relies on the analysis of the thickness of the retinal nerve fibre layer (RNFL) by optical coherence tomography (OCT). However, false positives and false negatives in the detection of RNFL abnormalities are common. Here we show that an algorithm integrating measurements of RNFL thickness and reflectance from standard wide-field OCT scans can be used to uncover the trajectories and optical texture of individual axonal fibre bundles in the retina and to discern distinctive patterns of loss of axonal fibre bundles in glaucoma, compressive optic neuropathy, optic neuritis and non-arteritic anterior ischaemic optic neuropathy. Such optical texture analysis can detect focal RNFL defects in early optic neuropathy, as well as residual axonal fibre bundles in end-stage optic neuropathy that were indiscernible by conventional OCT analysis and by red-free RNFL photography. In a diagnostic-performance study, optical texture analysis of the RNFL outperformed conventional OCT in the detection of glaucoma, as defined by visual-field testing or red-free photography. Our findings show that optical texture analysis of the RNFL for the detection of optic neuropathies is highly sensitive and specific

A hybrid deep learning approach for texture analysis

Texture classification is a problem that has various applications such as remote sensing and forest species recognition. Solutions tend to be custom fit to the dataset used but fails to generalize. The Convolutional Neural Network (CNN) in combination with Support Vector Machine (SVM) form a robust selection between powerful invariant feature extractor and accurate classifier. The fusion of classifiers shows the stability of classification among different datasets and slight improvement compared to state of the art methods. The classifiers are fused using confusion matrix after independent training of each using the same training set, then put to test. Statistical information about each classifier is fed to a confusion matrix that generates two confidence measures used in building two binary classifiers. The binary classifier is allowed to activate or deactivate a classifier during testing time based on a confidence measure obtained from the confusion matrix. The method obtained results approaching state of the art with a difference less than 1% in classification success rates. Moreover, the method was able to maintain this success rate among different datasets while other methods had failed to obtain similar stability. Two datasets had been used in this research Brodatz and Kylberg where the results came 98.17% and 99.70%. In comparison to conventional methods in the literature, it came as 98.9% and 99.64% respectively

Visual Prototyping of Cloth

Realistic visualization of cloth has many applications in computer graphics. An ongoing research problem is how to best represent and capture appearance models of cloth, especially when considering computer aided design of cloth. Previous methods can be used to produce highly realistic images, however, possibilities for cloth-editing are either restricted or require the measurement of large material databases to capture all variations of cloth samples. We propose a pipeline for designing the appearance of cloth directly based on those elements that can be changed within the production process. These are optical properties of fibers, geometrical properties of yarns and compositional elements such as weave patterns. We introduce a geometric yarn model, integrating state-of-the-art textile research. We further present an approach to reverse engineer cloth and estimate parameters for a procedural cloth model from single images. This includes the automatic estimation of yarn paths, yarn widths, their variation and a weave pattern. We demonstrate that we are able to match the appearance of original cloth samples in an input photograph for several examples. Parameters of our model are fully editable, enabling intuitive appearance design. Unfortunately, such explicit fiber-based models can only be used to render small cloth samples, due to large storage requirements. Recently, bidirectional texture functions (BTFs) have become popular for efficient photo-realistic rendering of materials. We present a rendering approach combining the strength of a procedural model of micro-geometry with the efficiency of BTFs. We propose a method for the computation of synthetic BTFs using Monte Carlo path tracing of micro-geometry. We observe that BTFs usually consist of many similar apparent bidirectional reflectance distribution functions (ABRDFs). By exploiting structural self-similarity, we can reduce rendering times by one order of magnitude. This is done in a process we call non-local image reconstruction, which has been inspired by non-local means filtering. Our results indicate that synthesizing BTFs is highly practical and may currently only take a few minutes for small BTFs. We finally propose a novel and general approach to physically accurate rendering of large cloth samples. By using a statistical volumetric model, approximating the distribution of yarn fibers, a prohibitively costly, explicit geometric representation is avoided. As a result, accurate rendering of even large pieces of fabrics becomes practical without sacrificing much generality compared to fiber-based techniques

Skin texture features for face recognition

Face recognition has been deployed in a wide range of important applications including surveillance and forensic identification. However, it still seems to be a challenging problem as its performance severely degrades under illumination, pose and expression variations, as well as with occlusions, and aging. In this thesis, we have investigated the use of local facial skin data as a source of biometric information to improve human recognition. Skin texture features have been exploited in three major tasks, which include (i) improving the performance of conventional face recognition systems, (ii) building an adaptive skin-based face recognition system, and (iii) dealing with circumstances when a full view of the face may not be avai'lable. Additionally, a fully automated scheme is presented for localizing eyes and mouth and segmenting four facial regions: forehead, right cheek, left cheek and chin. These four regions are divided into nonoverlapping patches with equal size. A novel skin/non-skin classifier is proposed for detecting patches containing only skin texture and therefore detecting the pure-skin regions. Experiments using the XM2VTS database indicate that the forehead region has the most significant biometric information. The use of forehead texture features improves the rank-l identification of Eigenfaces system from 77.63% to 84.07%. The rank-l identification is equal 93.56% when this region is fused with Kernel Direct Discriminant Analysis algorithm

Skin Texture as a Source of Biometric Information

Traditional face recognition systems have achieved remarkable performances when the whole face image is available. However, recognising people from partial view of their facial image is a challenging task. Face recognition systems' performances may also be degraded due to low resolution image quality. These limitations can restrict the practicality of such systems in real-world scenarios such as surveillance, and forensic applications. Therefore, there is a need to identify people from whatever information is available and one of the possible approaches would be to use the texture information from available facial skin regions for the biometric identification of individuals. This thesis presents the design, implementation and experimental evaluation of an automated skin-based biometric framework. The proposed system exploits the skin information from facial regions for person recognition. Such a system is applicable where only a partial view of a face is captured by imaging devices. The system automatically detects the regions of interest by using a set of facial landmarks. Four regions were investigated in this study: forehead, right cheek, left cheek, and chin. A skin purity assessment scheme determines whether the region of interest contains enough skin pixels for biometric analysis. Texture features were extracted from non-overlapping sub-regions and categorised using a number of classification schemes. To further improve the reliability of the system, the study also investigated various techniques to deal with the challenge where the face images may be acquired at different resolutions to that available at the time of enrolment or sub-regions themselves be partially occluded. The study also presented an adaptive scheme for exploiting the available information from the corrupt regions of interest. Extensive experiments were conducted using publicly available databases to evaluate both the performance of the prototype system and the adaptive framework for different operational conditions, such as level of occlusion and mixture of different resolution skin images. Results suggest that skin information can provide useful discriminative characteristics for individual identification. The comparison analyses with state-of-the-art methods show that the proposed system achieved a promising performance