3D Geometric and Haptic Modeling of Hand-Woven Textile Artifacts

Haptic Modeling of textile has attracted significant interest over the last decade. In spite of extensive research, no generic system has yet been proposed. The majority of the haptic models developed in the previous work assume a 2D mesh model for the textile which does not represent the real geometric configuration of the textile. In addition, they are based on empirical parameters obtained from textile samples using specialized instruments. The process is often time consuming and elaborate, consisting of manual measurement of physical and mechanical properties of the artifacts. The development of a generic approach for 3D haptic modeling of hand-woven textile artifacts is pursued in this work. In the proposed approach, the textile pattern and structure are recognized by digital processing of the artifact still image. A fuzzy-rule based expert system is developed to perform the recognition process. The data obtained in this process is employed to automatically generate the 3D geometric model of the artifact in VRML. The mechanical properties of the artifact are estimated by processing the textile geometric characteristics and yarn properties in a neural network system. These mechanical properties are then deployed in the construction of the textile mechanical model. The mechanical model is superimposed over the 3D geometric model to construct the haptic model. The proposed system is validated through both subjective and objective methods using a number of artifact samples. An extensive review of the published literature on the haptic modeling of textile is provided in the thesis. The benefits of textile haptic modeling are identified. Applications of existing models are reviewed and the significance and unique contribution of the work is presented. The image processing method and the fuzzy rule based expert system deployed in the construction of the geometric model are described in detail. The outcome is a 3D geometric model of the artifact in VRML which could be explored in a virtual reality world viewer. Similarly, the neural network model designed to estimate the mechanical characteristics of an artifact is presented the results of the training and validation of the model are provided.