Texture Synthesis by Fixed Neighborhood Searching a Dissertation Submitted to the Department of Electrical Engineering and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

Textures can describe a wide variety of natural phenomena with random variations over repeating patterns. Examples of textures include images, motions, and surface geometry. Since reproducing the realism of the physical world is a major goal for computer graphics, textures are important for rendering synthetic images and animations. However, because textures are so diverse it is difficult to describe and reproduce them under a common framework. In this thesis, we present new methods for synthesizing textures. The first part of the thesis is concerned with a basic algorithm for reproducing image textures. 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. The algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. Because of the use of this deterministic searching, our algorithm can avoid the computational demand of probability sampling and can be directly accelerated by a point searching algorithm such as tree-structured vector quantization. The second part of the thesis concerns various extensions and applications of our texture synthesis algorithm. Texture synthesis can be used to remove undesirable artifacts in photographs and films such as scratches, wires, pops, or scrambled regions. We extend our algorithm for this purpose by replacing artifacts with textured backgrounds via constrained synthesis. In addition to 2D images, textures can also be used to model other physical phenomena such as 3D temporal textures such as fire, smoke, and ocean waves, as well as 1D articulated motion signals such as walking and running. Despite the diversity of the dimensionality and generation process of these textures, out algorithm is capable of modeling and