Publications related to Inferring Assembly Objects and Sequences from Demonstrations

Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

Blind and universal image denoising consists of a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded ...

2019

A Bit Too Much? High Speed Imaging from Sparse Photon Counts

Edoardo Charbon, Claudio Bruschini, Samuel Burri

Recent advances in photographic sensing technologies have made it possible to achieve light detection in terms of a single photon. Photon counting sensors are being increasingly used in many diverse applications. We address the problem of jointly recoverin ...

IEEE2019

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

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

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

SGAN: An Alternative Training of Generative Adversarial Networks

François Fleuret, Tatjana Chavdarova

The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this success, they gained a reputation for being difficult to train, what results ...

IEEE2018

Vision-based detection of aircrafts and UAVs

Artem Rozantsev

Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects inc ...

EPFL2017

Analysis of CMS noise reduction for 65 nm CIS

Christian Enz, Assim Boukhayma, Raffaele Capoccia

This work explores the combination of a downscaled technology with in-pixel source-follower (SF) optimization, a high column-level gain and an analog implementation of Correlated-Multiple-Sampling (CMS) for noise reduction of CIS readout chains. Transient ...

2017

A method for reducing noise in measurements taken by a distributed sensor

Marcelo Alfonso Soto Hernandez

According to the present invention there is provided a method of sensing comprising the steps of, (a) acquiring plurality of measurement values using a distributed optical fibre sensor; (b) arranging the plurality of measurement values in a matrix having a ...

2016

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

Inferring Assembly Objects and Sequences from Demonstrations

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Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

A Bit Too Much? High Speed Imaging from Sparse Photon Counts

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

Mean-Field methods for Structured Deep-Learning in Computer Vision

Investigating Depth Domain Adaptation for Efficient Human Pose Estimation

SGAN: An Alternative Training of Generative Adversarial Networks

Vision-based detection of aircrafts and UAVs

Analysis of CMS noise reduction for 65 nm CIS

A method for reducing noise in measurements taken by a distributed sensor

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Vision-based detection of aircrafts and UAVs

Investigating Depth Domain Adaptation for Efficient Human Pose Estimation

A Bit Too Much? High Speed Imaging from Sparse Photon Counts

Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

Analysis of CMS noise reduction for 65 nm CIS

A method for reducing noise in measurements taken by a distributed sensor

Mean-Field methods for Structured Deep-Learning in Computer Vision

Sampling and Reconstruction of Shapes with Algebraic Boundaries

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

SGAN: An Alternative Training of Generative Adversarial Networks