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

A VISION-BASED QUALITY INSPECTION SYSTEM FOR FABRIC DEFECT DETECTION AND CLASSIFICATION

Published ThesisQuality inspection of textile products is an important issue for fabric manufacturers. It is desirable to produce the highest quality goods in the shortest amount of time possible. Fabric faults or defects are responsible for nearly 85% of the defects found by the garment industry. Manufacturers recover only 45 to 65% of their profits from second or off-quality goods. There is a need for reliable automated woven fabric inspection methods in the textile industry. Numerous methods have been proposed for detecting defects in textile. The methods are generally grouped into three main categories according to the techniques they use for texture feature extraction, namely statistical approaches, spectral approaches and model-based approaches. In this thesis, we study one method from each category and propose their combinations in order to get improved fabric defect detection and classification accuracy. The three chosen methods are the grey level co-occurrence matrix (GLCM) from the statistical category, the wavelet transform from the spectral category and the Markov random field (MRF) from the model-based category. We identify the most effective texture features for each of those methods and for different fabric types in order to combine them. Using GLCM, we identify the optimal number of features, the optimal quantisation level of the original image and the optimal intersample distance to use. We identify the optimal GLCM features for different types of fabrics and for three different classifiers. Using the wavelet transform, we compare the defect detection and classification performance of features derived from the undecimated discrete wavelet and those derived from the dual-tree complex wavelet transform. We identify the best features for different types of fabrics. Using the Markov random field, we study the performance for fabric defect detection and classification of features derived from different models of Gaussian Markov random fields of order from 1 through 9. For each fabric type we identify the best model order. Finally, we propose three combination schemes of the best features identified from the three methods and study their fabric detection and classification performance. They lead generally to improved performance as compared to the individual methods, but two of them need further improvement

Energy-Efficiency of Conveyor Belts in Raw Materials Industry

This book focuses on research related to the energy efficiency of conveyor transportation. The solutions presented in the Special Issue have an impact on optimizing, and thus reducing, the costs of energy consumption by belt conveyors. This is due, inter alia, to the use of better materials for conveyor belts, which reduce its rolling resistance and noise, and improve its ability to adsorb the impact energy from the material falling on the belt. The use of mobile robots designed to detect defects in the conveyor's components makes the conveyor operation safer, and means that the conveyor works for longer and there are no unplanned stops due to damage

Middle Byzantine silk in context: integrating the textual and material evidence

This work represents the most comprehensive investigation of silk in the middle Byzantine period to date. The current interpretation of silk as an imperial prerogative confined to elite use is poorly integrated with the body of evidence and lacks explanatory value. The difficult terminology and scattered mentions in written sources limits application of conventional research methods. Although a number of silk fragments survive in institutional collections, the lack of find and contextual information represents a formidable obstacle. This dissertation redefines silk in Byzantium by demonstrating its social importance, contribution to technology development, and integration in the regional economy. Findings are based on intensive analysis of production and consumption data from parallel investigation of texts and textile fragments according to a common framework. To aid data collection and analysis, information technology tools involving relational database methods and digital imaging were devised for this purpose. The evidence suggests that the historical process involving silk was shaped by a continuing cycle of elite differentiation and imitative reproduction, which contributed to the transmission of the material and production in the region. From a broader perspective, this work demonstrates the relevance of textile studies to the interpretation of economic and social history

Thermal protection properties of aerogel-coated Kevlar woven fabrics

This paper investigated the thermal properties of aerogel-coated Kevlar fabrics under both the ambient temperature and high temperature with laser radiation. It is found that the aerogels combined with a Kevlar fabric contribute to a higher thermal insulation value. Under laser radiation with high temperature, the aerogel content plays a vital role on the surface temperature of the fabrics. At laser radiations with pixel time 330 μs, the surface temperatures of the aerogel coated Kevlar fabrics are 400-440°C lower than that of the uncoated fabric. Results also show that the fabric temperature is directly proportional to pixel time. It can be concluded that the Kevlar fabrics coated with silica aerogel provides better thermal protection under high temperature

TRIZ Future Conference 2004

TRIZ the Theory of Inventive Problem Solving is a living science and a practical methodology: millions of patents have been examined to look for principles of innovation and patterns of excellence. Large and small companies are using TRIZ to solve problems and to develop strategies for future technologies. The TRIZ Future Conference is the annual meeting of the European TRIZ Association, with contributions from everywhere in the world. The aims of the 2004 edition are the integration of TRIZ with other methodologies and the dissemination of systematic innovation practices even through SMEs: a broad spectrum of subjects in several fields debated with experts, practitioners and TRIZ newcomers