Bidirectional texture function

Abstract: We present a simple image-based method of generating novel visual appearance in which a new image is synthesized by stitching together small patches of existing images. We call this process image quilting. First, we use quilting as a fast and very simple texture synthesis algorithm which produces surprisingly good results for a wide range of textures. Second, we extend the algorithm to perform texture transfer — rendering an object with a texture taken from a different object. More generally, we demonstrate how an image can be re-rendered in the style of a different image. The method works directly on the images and does not require 3D information.

Abstract: Texture synthesis is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results. In this paper, we present an efficient algorithm for realistic texture synthesis. The algorithm is easy to use and requires only a sample texture as input. It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.

Abstract: In this work, we investigate the visual appearance of real-world surfaces and the dependence of appearance on the geometry of imaging conditions. We discuss a new texture representation called the BTF (bidirectional texture function) which captures the variation in texture with illumination and viewing direction. We present a BTF database with image textures from over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions. We describe the methods involved in collecting the database as well as the importqance and uniqueness of this database for computer graphics. A related quantity to the BTF is the familiar BRDF (bidirectional reflectance distribution function). The measurement methods involved in the BTF database are conducive to simultaneous measurement of the BRDF. Accordingly, we also present a BRDF database with reflectance measurements for over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions. Both of these unique databases are publicly available and have important implications for computer graphics.

Abstract: : The problem of image texture analysis is introduced, and existing approaches are surveyed. An empirical evaluation method is applied to two texture measurement systems, co-occurrence statistics and augmented correlation statistics. A spatial-statistical class of texture measures is then defined and evaluated. It leads to a simple class of texture energy transforms, which perform better than any of the preceding methods. These transforms are very fast, and can be made invariant to changes in luminance, contrast, and rotation without histogram equalization or other preprocessing. Texture energy is measured by filtering with small masks, typically 5x5, then with a moving-window average of the absolute image values. This method, similar to human visual processing, is appropriate for textures with short coherence length or correlation distance. The filter masks are integer-valued and separable, and can be implemented with one-dimensional or 3x3 convolutions. The averaging operation is also very fast, with computing time independent of window size. Texture energy planes may be linearly combined to form a smaller number of discriminant planes. These principal component planes seem to represent natural texture dimensions, and to be more reliable texture measures than the texture energy planes. Texture segmentation or classification may be accomplished using either texture energy or principal component planes as input. This study classified 15x15 blocks of eight natural textures. Accuracies of 72% were achieved with co- occurrence statistics, 65% with augmented correlation statistics, and 94% with texture energy statistics.