Texture Synthesis

Import 05/08/2014Má bakalářská práce se zabývá syntézou textur. Zpočátku popisuji různé přístupy, jaké typy algoritmů syntézy textur se používaly nebo používají a jaké výhody a nevýhody poskytují. Pro implementaci jsme vybrali algoritmus syntézy textur vytvořený skupinou Microsoft Research s názvem Parallel Controllable Texture Synthesis[1], který implementuji v jazyce C++ za pomocí knihovny OpenCV. Tento algoritmus následně do detailů popisuji a vysvětluji, na jakém principu funguje a jak zásadně mohou vstupní hodnoty uživatele ovlivňovat výstupní textury vytvořené pomocí tohoto algoritmu. V kapitole čtvrté vyhodnocuji uměle vytvořené textury, dobu zpracování, kvalitu a jaké nastavení vstupních hodnot je nejlepší použít. V poslední kapitole lehce nastiňuji, čeho by chtěli vývojáři v oblasti syntézy textur ještě v budoucnosti dosáhnout.My bachelor thesis deals with texture synthesis. At first, I’m describing differences between different approaches and what is good and bad about them. For my implementation and testing purposes we chose one texture synthesis algorithm invented by Microsoft Research group, called Parallel Controllable Texture Synthesis [1]. I'm explaining and describing in detail how is this algorithm working and how the user is capable of affecting the output texture by the input values for example by adding more randomness etcetera. In the fourth chapter I analyze artificial texture, processing time and what user input values are the best to use. In the last chapter I lightly outline what the developers would like to achieve in the future with texture synthesis algorithms.460 - Katedra informatikyvýborn

Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture

This paper addresses the problem of interpolating visual textures. We formulate this problem by requiring (1) by-example controllability and (2) realistic and smooth interpolation among an arbitrary number of texture samples. To solve it we propose a neural network trained simultaneously on a reconstruction task and a generation task, which can project texture examples onto a latent space where they can be linearly interpolated and projected back onto the image domain, thus ensuring both intuitive control and realistic results. We show our method outperforms a number of baselines according to a comprehensive suite of metrics as well as a user study. We further show several applications based on our technique, which include texture brush, texture dissolve, and animal hybridization.Comment: Accepted to CVPR'1

Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints

This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results. More precisely, the texture synthesis is regarded as a constrained optimization problem, with constraints conditioning both the Fourier spectrum and statistical features learned by CNNs. In contrast with existing methods, the presented method inherits from previous CNN approaches the ability to depict local structures and fine scale details, and at the same time yields coherent large scale structures, even in the case of quasi-periodic images. This is done at no extra computational cost. Synthesis experiments on various images show a clear improvement compared to a recent state-of-the art method relying on CNN constraints only

Optimized synthesis of art patterns and layered textures

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Functionally heterogeneous porous scaffold design for tissue engineering

Most of the current tissue scaffolds are mainly designed with homogeneous porosity which does not represent the spatial heterogeneity found in actual tissues. Therefore engineering a realistic tissue scaffolds with properly graded properties to facilitate the mimicry of the complex elegance of native tissues are critical for the successful tissue regeneration. In this work, novel bio-mimetic heterogeneous porous scaffolds have been modeled. First, the geometry of the scaffold is extracted along with its internal regional heterogeneity. Then the model has been discretized with planner slices suitable for layer based fabrication. An optimum filament deposition angle has been determined for each slice based on the contour geometry and the internal heterogeneity. The internal region has been discritized considering the homogeneity factor along the deposition direction. Finally, an area weight based approach has been used to generate the spatial porosity function that determines the filament deposition location for desired biomimetic porosity. The proposed methodology has been implemented and illustrative examples are provided. The effective porosity has been compared between the proposed design and the conventional homogeneous scaffolds. The result shows a significant error reduction towards achieving the biomimetic porosity in the scaffold design and provides better control over the desired porosity level. Moreover, sample designed structures have also been fabricated with a NC motion controlled micro-nozzle biomaterial deposition system