CiteSeerX — Texture optimization for example-based synthesis.

Given an example texture, these methods produce a larger texture that is tailored to the user's needs. In this state-of-the-art report, we aim to achieve three goals: (1) provide a tutorial that is easy to follow for readers who are not already familiar with the subject.

Example-Based Model Synthesis Paul Merrell University of North Carolina at Chapel Hill (a) (b) Figure 1: From the example model (a), a larger model (b) is automatically created using model synthesis. Abstract Model synthesis is a new approach to 3D modeling which automat- ically generates large models that resemble a small example model.

We present a novel technique for texture synthesis using optimization. We define a Markov Random Field (MRF)-based similarity metric for measuring the quality of synthesized texture with respect to a given input sample.

Fast Example-based Surface Texture Synthesis via Discrete Optimization Abstract We synthesize and animate general texture pat-terns over arbitrary 3D mesh surfaces. The animation is con-trolled by o w elds over the target mesh, and the texture can be arbitrary user input as long it satises the Markov-Random-Field assumptions. We achieve this by.

Non-Local Texture Optimization With Wasserstein Regularization Under Convolutional Neural Network Abstract: Example-based texture synthesis aims to generate a new texture from an exemplar texture and has long been drawing attention in the fields of computer graphics, computer vision, and image processing. Nevertheless, synthesizing structured.

For example, in my essay, I use two examples that show how it's good if you do pay attention to details, but then I also use an example which shows why it is so bad if you do NOT pay attention to details. The content on Tiny Buddha is designed to support, not replace, medical or psychiatric treatment.

Most patch-based texture synthesis algorithms using Markov Random Field for composite materials only considers color similarity between the corresponding pixels. The traditional algorithms are lack of adaptability, so the size of patches needs to be defined artificially in advance as the result of blurring of image texture features for composite materials.

Example-Based Style Synthesis Iddo Drori Daniel Cohen-Or Hezy Yeshurun School of Computer Science Tel Aviv University Tel Aviv, Israel, 69978 Abstract We introduce an example-based synthesis technique that extrapolates novel styles for a given input image. The tech-nique is based on separating the style and content of image fragments.

The goal of this course was to implement a texture synthesis algorithm and analyze its performance and visual quality. Two papers form the theoretical foundation of this project: “Texture Optimization for Example-based Synthesis” by Kwatra et al (KEBK05) and “Patchmatch: A randomized Correspondence Algorithm for Structural Image Editing” by Barnes et al (BSFG09).

Texture synthesis is the process of algorithmically constructing a large digital image from a small digital sample image by taking advantage of its structural content. It is an object of research in computer graphics and is used in many fields, amongst others digital image editing, 3D computer graphics and post-production of films.

We present tile-based methods for generating Poisson disk distributions and for synthesizing textures. Tile-based methods not only allow to efficiently generate Poisson disk distributions and synthesize textures, but also enable new applications such as tile-based texture synthesis and a procedural object distribution function.

One well-known technique that falls in this category is example-based texture synthesis, in which arbitrarily large textures are created based on a small “exemplar” texture. (See for example: State of the Art in Example-based Texture Synthesis ) In that technique, the goal is to generate new textures that look like plausible extensions of the exemplar texture.