Image-based and model-based methods are two representative rendering methods for generating virtual images of objects from their real images. Extensive research on these two methods has been made in CV and CG communities. However, both methods still have several drawbacks when it comes to applying them to the mixed reality where we integrate such virtual images with real background images. To overcome these difficulties, we propose a new method, which we refer to as the Eigen-Texture method. The proposed method samples appearances of a real object under various illumination and viewing conditions, and compresses them in the 2D coordinate system defined on the 3D model surface. The 3D model is generated from a sequence of range images. The Eigen-Texture method is practical because it does not require any detailed reflectance analysis of the object surface, and has great advantages due to the accurate 3D geometric models. This paper describes the method, and reports on its implementation...

The bidirectional texture function (BTF) is a 6D function that can describe textures arising from both spatially-variant surface reflectance and surface mesostructures. In this paper, we present an algorithm for synthesizing the BTF on an arbitrary surface from a sample BTF. A main challenge in surface BTF synthesis is the requirement of a consistent mesostructure on the surface, and to achieve that we must handle the large amount of data in a BTF sample. Our algorithm performs BTF synthesis based on surface textons, which extract essential information from the sample BTF to facilitate the synthesis. We also describe a general search strategy, called the �-coherent search, for fast BTF synthesis using surface textons. A BTF synthesized using our algorithm not only looks similar to the BTF sample in all viewing/lighthing conditions but also exhibits a consistent mesostructure when viewing and lighting directions change. Moreover, the synthesized BTF fits the target surface naturally and seamlessly. We demonstrate the effectiveness of our algorithm with sample BTFs from various sources, including those measured from real-world textures.

This paper studies the recovery of shape from texture under perspective projection. We regard Shape from Texture as a statistical estimation problem, the texture being the realization of a stochastic process. We introduce warplets, which generalize wavelets over the 2D ane group. At ne scales, the warpogram of the image obeys a transport equation, called Texture Gradient Equation.

Texture as a surface representation is the subject of a wide body of computer vision and computer graphics literature. While texture is always associated with a form of repetition in the image, the repeating quantity may vary. The texture may be a color or albedo variation as in a checkerboard, a paisley print or zebra stripes. Very often in real-world scenes, texture is instead due to a surface height variation, e.g. pebbles, gravel, foliage and any rough surface. Such surfaces are referred to here as 3D textured surfaces. Standard texture recognition algorithms are not appropriate for 3D textured surfaces because the appearance of 3D textured surfaces changes in a complex manner with viewing direction, illumination direction and scale. Recent methods have been developed for recognition of 3D textured surfaces using a database of surfaces observed under varied imaging parameters. One of these methods is based on 3D textons obtained using K-means clustering of multiscale feature vectors. Another method uses eigen-analysis originally developed for appearance-based object recognition. In this work we develop a hybrid approach that employs both feature grouping and dimensionality reduction. The method is tested using the Columbia-Utrecht texture database (CUReT) and provides excellent recognition rates. The method is compared with existing recognition methods for 3D textured surfaces. A direct comparison is facilitated by empirical recognition rates from the same texture data set. The current method has key advantages over existing methods including requiring less apriori information on both the training and novel images. 1

Quantitative characterization of skin appearance is an important but difficult task. The skin surface is a detailed landscape, with features that depend on many variables such as body location (knuckle vs. torso), subject parameters (age/gender/health) and imaging parameters (lighting and camera). Computational modeling of skin texture has potential uses in many fields and applications including realistic rendering for computer graphics, robust face models for computer vision, computer assisted diagnosis for dermatology, topical drug efficacy testing for the pharmaceutical industry and quantitative product comparison for cosmetics. In this work, image-based representations of skin appearance are used in order to have descriptive capabilities without the need for prohibitively complex physics-based skin models. We present a method for representing and recognizing different areas of the skin surface that have visibly different texture properties. Our model takes into account the varied appearance of the skin with changes in illumination and viewing directions. I.

Shape from texture has received a great deal of attention in the past few decades. This paper analyzes the spectral variations of texture spatial frequencies as a function of orientation and depth of a 3-D planar surface. Based on this relationship we attempt to derive an expression for the extraction of 3-D surface orientation using texture features alone. Using experimentation on simulated texture images, we illustrate the advantage of using 1-D wavelets for this purpose.

Texton models have proven to be very discriminative for the recognition of grayvalue images taken from rough textures. To further improve the discriminative power of the distinctive texton models of Varma and Zisserman (VZ model) (IJCV, vol. 62(1), pp. 61-81, 2005), we propose two schemes to exploit color information. First, we incorporate color information directly at the texton level, and apply color invariants to deal with straightforward illumination effects as local intensity, shading and shadow. But, the learning of representatives of the spatial structure and colors of textures may be hampered by the wide variety of apparent structure-color combinations. Therefore, our second contribution is an alternative approach, where we weight grayvalue-based textons with color information in a post-processing step, leaving the originalVZ algorithm intact. We demonstrate that the color-weighted textons outperform the VZ textons as well as the color invariant textons. The color-weighted textons are specifically more discriminative than grayvalue-based textons when the size of the example image set is reduced. When using 2 example images only, recognition performance is 85.6%, which is an improvement over grayvaluebased textons of 10%. Hence, incorporating color in textons facilitates the learning of textons. 1