Search

Quantitative characterization of surface appearance is an important but difficult task. Surfaces of real world objects are detailed landscapes, with complex geometry and local optical properties. Surface appearance is strongly affected by the direction from which it is viewed and illuminated. Computational modeling of surface texture has potential uses in many applications including realistic rendering for computer graphics and robust recognition for computer vision. For recognition, the overall structure of the object is important, but fine-scale details can assist the recognition problem greatly. We develop models of surface texture and demonstrate their use in recognition tasks. We also describe a texture camera for capturing fine-scale surface details. Specifically, the texture camera measures reflectance and surface height variation using curved mirrors. We discuss why measurements and models of fine scale detail are important in modern industrial applications. [Copyright &y& Elsevier]

In vision and graphics, advanced object models require not only 3D shape, but also surface detail. While several scanning devices exist to capture the global shape of an object, few methods concentrate on capturing the fine-scale detail. Fine-scale surface geometry (relief texture), such as surface markings, roughness, and imprints, is essential in highly realistic rendering and accurate prediction. We present a novel approach for measuring the relief texture of specular or partially specular surfaces using a specialized imaging device with a concave parabolic mirror to view multiple angles in a single image. Laser scanning typically fails for specular surfaces because of light scattering, but our method is explicitly designed for specular surfaces. Also, the spatial resolution of the measured geometry is significantly higher than standard methods, so very small surface details are captured. Furthermore, spatially varying reflectance is measured simultaneously, i.e., both texture color and texture shape are retrieved. [ABSTRACT FROM AUTHOR]

In this paper, we present a method of skin imaging called bidirectional imaging that captures significantly more properties of appearance than standard imaging. The observed structure of the skin's surface is greatly dependent on the angle of incident illumination and the angle of observation. Specific protocols to achieve bidirectional imaging are presented and used to create the Rutgers Skin Texture Database (clinical component). This image database is the first of its kind in the dermatology community. Skin images of several disorders under multiple controlled illumination and viewing directions are provided publicly for research and educational use. Using this skin texture database, we employ computational surface modeling to perform automated skin texture classification. The classification experiments demonstrate the usefulness of the modeling and measurement methods. [ABSTRACT FROM AUTHOR]