Publications related to Combining Geometric and Appearance Priors for Robust Homography Estimation

Aggregating Spatial and Photometric Context for Photometric Stereo

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

EPFL2024

Progressive Correspondence Pruning by Consensus Learning

Mathieu Salzmann, Hongwei Li, Chenxu Zhao, Feng Zhu, Rui Zhao

Correspondence pruning aims to correctly remove false matches (outliers) from an initial set of putative correspondences. The pruning process is challenging since putative matches are typically extremely unbalanced, largely dominated by outliers, and the r ...

IEEE2021

Real-time camera pose estimation for sports fields

Pascal Fua, Charles Dubout, Horesh Beny Ben Shitrit, Pablo Marquez Neila, Stefano Savarè, Leonardo Citraro

Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image ...

2020

Multi-View Triangulation: Systematic Comparison and an Improved Method

Peng Song

Triangulation is an important task in the 3D reconstruction of computer vision. It seems simple to find the position of a point in 3D space when its 2D perspective projections in multi-view images are given and the corresponding camera projection matrices ...

2020

Bayesian regression explains how human participants handle parameter uncertainty

Jean-Pascal Théodor Pfister, Michael Herzog, Maya Anna Jastrzebowska, Mattew Pachai

Author summary How do humans make prediction when the critical factor that influences the quality of the prediction is hidden? Here, we address this question by conducting a simple psychophysical experiment in which participants had to extrapolate a parabo ...

2020

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks

Konstantinos Pitas

Deep neural networks have been empirically successful in a variety of tasks, however their theoretical understanding is still poor. In particular, modern deep neural networks have many more parameters than training data. Thus, in principle they should over ...

EPFL2020

Gaussian Process Regression for Maximum Entropy Distribution

Mohammadhossein Gorji, Mohsen Sadr

Maximum-Entropy Distributions offer an attractive family of probability densities suitable for moment closure problems. Yet finding the Lagrange multipliers which parametrize these distributions, turns out to be a computational bottleneck for practical clo ...

2020

An Inertial Parallel and Asynchronous Forward-Backward Iteration for Distributed Convex Optimization

Georgios Stathopoulos, Colin Neil Jones

Two characteristics that make convex decomposition algorithms attractive are simplicity of operations and generation of parallelizable structures. In principle, these schemes require that all coordinates update at the same time, i.e., they are synchronous ...

SPRINGER/PLENUM PUBLISHERS2019

Geometry in active learning for binary and multi-class image segmentation

Pascal Fua, Raphael Sznitman, Ksenia Konyushkova

We propose an active learning approach to image segmentation that exploits geometric priors to speed up and streamline the annotation process. It can be applied for both background foreground and multi-class segmentation tasks in 2D images and 3D image vol ...

2019

Gaussian Process Regression for Maximum Entropy Distribution

Mohsen Sadr

Maximum-Entropy Distributions offer an attractive family of probability densities suitable for moment closure problems. Yet finding the Lagrange multipliers which parametrize these distributions, turns out to be a computational bottleneck for practical clo ...

2019

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

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

Sparse Gradient Optimization and its Applications in Image Processing

Nikolaos Arvanitopoulos Darginis

Millions of digital images are captured by imaging devices on a daily basis. The way imaging devices operate follows an integral process from which the information of the original scene needs to be estimated. The estimation is done by inverting the integra ...

EPFL2017

Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras

Pascal Fua, Sudipta Sinha, Artem Rozantsev

We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a si ...

Ieee2017

Automated Reconstruction of Evolving Curvilinear Tree Structures

Przemyslaw Rafal Glowacki

Curvilinear networks are prevalent in nature and span many different scales, ranging from micron-scale neural structures in the brain to petameter-scale dark-matter arbors binding massive galaxy clusters. Reliably reconstructing them in an automated fashio ...

EPFL2016

Registration of single landscape photographs with 3D landscape models

Timothée Produit

Terrestrial imagery found in public databases is an alternative and complementary data source for environmental studies. Compared to usual data source, such as airborne images, terrestrial pictures may have higher temporal and spatial resolutions. They are ...

EPFL2015

Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies

Jean-Pierre Hubaux, Florian Tramèr, Erman Ayday, Zhicong Huang

Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy guarantees than traditional statistical methods. However, recent studies have shown that for reasonable privacy budgets, differential priva ...

2015

Geometric Variational Models for Inverse Problems in Imaging

Virginia Estellers Casas

This dissertation develops geometric variational models for different inverse problems in imaging that are ill-posed, designing at the same time efficient numerical algorithms to compute their solutions. Variational methods solve inverse problems by the fo ...

EPFL2013

Automated Reconstruction of Curvilinear Networks from 2D and 3D Imagery

Engin Türetken

Reconstructing complex curvilinear structures such as neural circuits, road networks, and blood vessels is a key challenge in many scientific and engineering fields. It has a broad range of applications, from the delineation of micrometer-sized neurons in ...

EPFL2013

Pose estimation of landscape images using DEM and orthophotos

François Golay, Devis Tuia, Timothée Produit, Christoph Strecha

In this paper, we propose a methodology for the estimation of the pose of oblique landscape images. Knowledge about the pose is needed for using such images in augmented reality applications or to allow projection of pixels in a GIS for spatial analysis. W ...

2012