Publications related to Adapting the Sample Size in Texture Synthesis

Pre-annealing for improved LPCVD deposited boron-doped poly-Si hole-selective contacts

Franz-Josef Haug, Luca Massimiliano Antognini, Josua Andreas Stückelberger, Xinyu Zhang, Zhao Wang, Jie Yang

We demonstrate the beneficial effect of a pre-annealing step prior to the boron diffusion on passivation and contact resistivity of industrially LPCVD deposited poly-Si/SiOX hole-selective contacts. We investigate the influence of the pre-annealing tempera ...

ELSEVIER2023

Texture mapping and IFC material retrievement for virtual reality applications

Sergi Aguacil Moreno, Laurent Deschamps, Sebastian Duque Mahecha, Alexandre Denis Stoll

Most current software support the Industry Foundation Classes (IFC) transfer of Building Information Models (BIMs) to applications enabling Virtual Reality (VR) navigation (BuildingSMART, 2020; Kiviniemi, Tarandi, Karlshøj, Bell, and Karud, 2008; Poljanšek ...

2021

Differentiable inverse rendering based on radiative backpropagation

Wenzel Alban Jakob, Merlin Eléazar Nimier-David

A computer-implemented inverse rendering method is provided. The method comprises: computing an adjoint image by differentiating an objective function that evaluates the quality of a rendered image, image elements of the adjoint image encoding the sensitiv ...

2021

Light Path Gradients for Forward and Inverse Rendering

Tizian Lucien Zeltner

Physically based rendering is a process for photorealistic digital image synthesis and one of the core problems in computer graphics. It involves simulating the light transport, i.e. the emission, propagation, and scattering of light through a virtual scen ...

EPFL2021

Wide Gamut Spectral Upsampling with Fluorescence

Wenzel Alban Jakob

Physically based spectral rendering has become increasingly important in recent years. However, asset textures in such systems are usually still drawn or acquired as RGB tristimulus values. While a number of RGB to spectrum upsampling techniques are availa ...

2019

SUBDIVIDE AND CONQUER Active Contours and Surfaces for Biomedical Image Segmentation

Anaïs Laure Marie-Thérèse Badoual

Ongoing advances in imaging techniques create new demands regarding the analysis of images in medicine and biology. Image segmentation is a key step of many image analysis pipelines and its proper execution is a particularly challenging task. This thesis i ...

EPFL2019

Motion Vector Estimation of Textureless Objects Exploiting Reaction-Diffusion Cellular Automata

Alexandre Schmid, Kazuyoshi Ishimura

Conventional motion estimation algorithms extract motion vectors from image sequences based on the image's local-brightness differences in consecutive images. Therefore, motion vectors are extracted along the moving edges formed by moving objects over thei ...

Old City Publishing Inc2016

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Martin Vetterli, Arash Amini, Mitra Fatemi

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of

\mathds{1}_{\{p\leq0\}}

to the image plane, where

\mathds{1}

denotes the indicator function and

p

is some r ...

2016

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

Self Tuning Texture Optimization

Mark Pauly, Boris Neubert, Alexandre Kaspar

The goal of example-based texture synthesis methods is to generate arbitrarily large textures from limited exemplars in order to fit the exact dimensions and resolution required for a specific modeling task. The challenge is to faithfully capture all of th ...

Wiley-Blackwell2015

Adapting the Sample Size in Texture Synthesis

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Pre-annealing for improved LPCVD deposited boron-doped poly-Si hole-selective contacts

Texture mapping and IFC material retrievement for virtual reality applications

Differentiable inverse rendering based on radiative backpropagation

Light Path Gradients for Forward and Inverse Rendering

Wide Gamut Spectral Upsampling with Fluorescence

SUBDIVIDE AND CONQUER Active Contours and Surfaces for Biomedical Image Segmentation

Motion Vector Estimation of Textureless Objects Exploiting Reaction-Diffusion Cellular Automata

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Shapes from Pixels

Self Tuning Texture Optimization

Light Path Gradients for Forward and Inverse Rendering

Pre-annealing for improved LPCVD deposited boron-doped poly-Si hole-selective contacts

Texture mapping and IFC material retrievement for virtual reality applications

SUBDIVIDE AND CONQUER Active Contours and Surfaces for Biomedical Image Segmentation

Shapes from Pixels

Wide Gamut Spectral Upsampling with Fluorescence

Self Tuning Texture Optimization

Differentiable inverse rendering based on radiative backpropagation

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Motion Vector Estimation of Textureless Objects Exploiting Reaction-Diffusion Cellular Automata