Publications related to Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer

Investigating Depth Domain Adaptation for Efficient Human Pose Estimation

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

Convolutional Neural Networks (CNN) are the leading models for human body landmark detection from RGB vision data. However, as such models require high computational load, an alternative is to rely on depth images which, due to their more simple nature, ca ...

2018

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Bugra Tekin

Estimating the 3D poses of rigid and articulated bodies is one of the fundamental problems of Computer Vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. Despite the ever-growin ...

EPFL2018

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Bin Jin

Imaging devices have become ubiquitous in modern life, and many of us capture an increasing number of images every day. When we choose to share or store some of these images, our primary selection criterion is to choose the most visually pleasing ones. Yet ...

EPFL2018

Design rules for Unipolar Unicolor Coded Brillouin Optical Time Domain Analysis

Luc Thévenaz, Marcelo Alfonso Soto Hernandez, Zhisheng Yang, Simon Adrien Zaslawski, Zonglei Li

The performance of unipolar unicolor coded Brillouin optical time-domain analysis (BOTDA) is thoroughly studied, providing quantitative guidelines to design a distortion-free coded BOTDA system operating at maximum signal-to-noise ratio. ...

Optical Society of America2018

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

EPFL2017

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

Sciences et Techniques de l’Ingénieur (STI)2017

Privacy-Friendly Photo Sharing and Relevant Applications Beyond

Lin Yuan

Popularization of online photo sharing brings people great convenience, but has also raised concerns for privacy. Researchers proposed various approaches to enable image privacy, most of which focus on encrypting or distorting image visual content. In this ...

EPFL2017

Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration

Luc Thévenaz, Marcelo Alfonso Soto Hernandez

Distributed optical fibre sensors possess the unique capability of measuring the spatial and temporal map of environmental quantities that can be of great interest for several field applications. Although existing methods for performance enhancement have e ...

Nature Publishing Group2016

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

We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth imag ...

Springer2016

Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer

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Investigating Depth Domain Adaptation for Efficient Human Pose Estimation

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Design rules for Unipolar Unicolor Coded Brillouin Optical Time Domain Analysis

Large-Scale Image Segmentation with Convolutional Networks

Large-Scale Image Segmentation with Convolutional Networks

Privacy-Friendly Photo Sharing and Relevant Applications Beyond

Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Learning Robust Features and Latent Representations for Single View 3D Pose Estimation of Humans and Objects

Computational Aesthetics and Image Enhancements using Deep Neural Networks

Design rules for Unipolar Unicolor Coded Brillouin Optical Time Domain Analysis

Large-Scale Image Segmentation with Convolutional Networks

Large-Scale Image Segmentation with Convolutional Networks

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Intensifying the response of distributed optical fibre sensors using 2D and 3D image restoration

Investigating Depth Domain Adaptation for Efficient Human Pose Estimation

Privacy-Friendly Photo Sharing and Relevant Applications Beyond