Enhancing the E-Commerce Experience through Haptic Feedback Interaction

The sense of touch is important in our everyday lives and its absence makes it difficult to explore and manipulate everyday objects. Existing online shopping practice lacks the opportunity for physical evaluation, that people often use and value when making product choices. However, with recent advances in haptic research and technology, it is possible to simulate various physical properties such as heaviness, softness, deformation, and temperature. The research described here investigates the use of haptic feedback interaction to enhance e-commerce product evaluation, particularly haptic weight and texture evaluation. While other properties are equally important, besides being fundamental to the shopping experience of many online products, weight and texture can be simulated using cost-effective devices. Two initial psychophysical experiments were conducted using free motion haptic exploration in order to more closely resemble conventional shopping. One experiment was to measure weight force thresholds and another to measure texture force thresholds. The measurements can provide better understanding of haptic device limitation for online shopping in terms of the availability of different stimuli to represent physical products. The outcomes of the initial psychophysical experimental studies were then used to produce various absolute stimuli that were used in a comparative experimental study to evaluate user experience of haptic product evaluation. Although free haptic exploration was exercised on both psychophysical experiments, results were relatively consistent with previous work on haptic discrimination. The threshold for weight force discrimination represented as downward forces was 10 percent. The threshold for texture force discrimination represented as friction forces was 14.1 percent, when using dynamic coefficient of friction at any level of static coefficient of friction. On the other hand, the comparative experimental study to evaluate user experience of haptic product information indicated that haptic product evaluation does not change user performance significantly. However, although there was an increase in the time taken to complete the task, the number of button click actions tended to decrease. The results showed that haptic product evaluation could significantly increase the confidence of shopping decision. Nevertheless, the availability of haptic product evaluation does not necessarily impose different product choices but it complements other selection criteria such as price and appearance. The research findings from this work are a first step towards exploring haptic-based environments in e-commerce environments. The findings not only lay the foundation for designing online haptic shopping but also provide empirical support to research in this direction

Hairstyle modelling based on a single image.

Hair is an important feature to form character appearance in both film and video game industry. Hair grooming and combing for virtual characters was traditionally an exclusive task for professional designers because of its requirements for both technical manipulation and artistic inspiration. However, this manual process is time-consuming and further limits the flexibility of customised hairstyle modelling. In addition, it is hard to manipulate virtual hairstyle due to intrinsic hair shape. The fast development of related industrial applications demand an intuitive tool for efficiently creating realistic hairstyle for non-professional users. Recently, image-based hair modelling has been investigated for generating realistic hairstyle. This thesis demonstrates a framework Struct2Hair that robustly captures a hairstyle from a single portrait input. Specifically, the 2D hair strands are traced from the input with the help of image processing enhancement first. Then the 2D hair sketch of a hairstyle on a coarse level is extracted from generated 2D hair strands by clustering. To solve the inherently ill-posed single-view reconstruction problem, a critical hair shape database has been built by analysing an existing hairstyle model database. The critical hair shapes is a group of hair strands which possess similar shape appearance and close space location. Once the prior shape knowledge is prepared, the hair shape descriptor (HSD) is introduced to encode the structure of the target hairstyle. The HSD is constructed by retrieving and matching corresponding critical hair shape centres in the database. The full-head hairstyle is reconstructed by uniformly diffusing the hair strands on the scalp surface under the guidance of extracted HSD. The produced results are evaluated and compared with the state-of-the-art image based hair modelling methods. The findings of this thesis lead to some promising applications such as blending hairstyles to populate novel hair model, editing hairstyle (adding fringe hair, curling and cutting/extending hairstyle) and a case study of Bas-relief hair modelling on pre-processed hair images