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Pascal Fua

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Publications associées (319)

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Learning Image Descriptors with the Boosting-Trick

Pascal Fua, Vincent Lepetit, Tomasz Piotr Trzcinski, Christos Marios Christoudias

In this paper we apply boosting to learn complex non-linear local visual feature representations, drawing inspiration from its successful application to visual object detection. The main goal of local feature descriptors is to distinctively repre- sent a s ...

2012

Beyond Cartesian Representations for Local Descriptors

Patrick Winfried Josef Ebel, Pascal Fua, Kwang Moo Yi, Eduard Trulls Fortuny, Anastasiia Mishchuk

The dominant approach for learning local patch descriptors relies on small image regions whose scale must be properly estimated a priori by a keypoint detector. In other words, if two patches are not in correspondence, their descriptors will not match. A s ...

IEEE COMPUTER SOC2019

Supervised Feature Learning for Curvilinear Structure Segmentation

Pascal Fua, Vincent Lepetit, Roberto Rigamonti, Carlos Joaquin Becker

Springer2013

Leveraging Self-Supervision for Cross-Domain Crowd Counting

Pascal Fua, Weizhe Liu, Nikita Durasov

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from ...

IEEE COMPUTER SOC2022

Learning Structured Models for Segmentation of 2D and 3D Imagery

Pascal Fua, Graham Knott, Carlos Joaquin Becker, Kevin Smith, Aurélien Lucchi, Pablo Marquez Neila, Yunpeng Li

Efficient and accurate segmentation of cellular structures in microscopic data is an essential task in medical imaging. Many state-of-the-art approaches to image segmentation use structured models whose parameters must be carefully chosen for optimal perfo ...

Institute of Electrical and Electronics Engineers2015

Fast Keypoint Recognition in Ten Lines of Code

Pascal Fua, Vincent Lepetit, Mustafa Özuysal

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in ...

IEEE2007

Simultaneous Segmentation and Anatomical Labeling of the Cerebral Vasculature

Pascal Fua, Engin Türetken

We present a novel algorithm for the simultaneous segmentation and anatomical labeling of the cerebral vasculature. The method first constructs an over-complete graph capturing the vasculature. It then selects and labels the subset of edges that most likel ...

2014

From Canonical Poses to 3D Motion Capture Using a Single Camera

Pascal Fua, Vincent Lepetit

We combine detection and tracking techniques to achieve robust 3D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera. We rely on detecting key postures, which can be done reliably, using a motion model to inf ...

Institute of Electrical and Electronics Engineers2010

Combining Geometric and Appearance Priors for Robust Homography Estimation

Pascal Fua, Vincent Lepetit, Mustafa Özuysal

The homography between pairs of images are typically computed from the correspondence of keypoints, which are established by using image descriptors. When these descriptors are not reliable, either because of repetitive patterns or large amounts of clutter ...

2010

Fast Keypoint Recognition using Random Ferns

Pascal Fua, Vincent Lepetit, Michael Calonder, Mustafa Özuysal

2010

Closed-Form Solution to Non-rigid 3D Surface Registration

Pascal Fua, Mathieu Salzmann, Vincent Lepetit

We present a closed-form solution to the problem of recovering the 3D shape of a non-rigid inelastic surface from 3D-to-2D correspondences. This lets us detect and reconstruct such a, surface by matching individual images against a reference configuration, ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2008

Garment Recovery with Shape and Deformation Priors

Pascal Fua, Benoît Alain René Guillard, Ren Li, Corentin Camille Marius Dumery

While modeling people wearing tight-fitting clothing has made great strides in recent years, loose-fitting clothing remains a challenge. We propose a method that delivers realistic garment models from real-world images, regardless of garment shape or defor ...

2024

DISK: learning local features with policy gradient

Pascal Fua, Eduard Trulls Fortuny, Michal Jan Tyszkiewicz

Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints. We introduce DISK (DIScrete Keypoints), a novel method that overcomes these obstacles by leveragin ...

2020

Spatio-Chromatic Decorrelation by Shift-Invariant Filtering

Pascal Fua, Sabine Süsstrunk, Matthew Brown

In this paper we derive convolutional filters for colour image whitening and decorrelation. Whilst whitening can be achieved via eigendecomposition of the image patch covariance, this operation is neither efficient nor biologically plausible. Given the shi ...

2011

Monocular 3D Reconstruction of Locally Textured Surfaces

Pascal Fua, Mathieu Salzmann, Appu Shaji, Aydin Varol

Most recent approaches to monocular non-rigid 3D shape recovery rely on exploiting point correspondences and work best when the whole surface is well-textured. The alternative is to rely either on contours or shading information, which has only been demons ...

2012

No Identity, no problem: Motion through detection for people tracking

Friedrich Wilke Grosche, Pascal Fua, Martin Pierre Engilberge

Tracking-by-detection has become the de facto standard approach to people tracking. To increase robustness, some approaches incorporate re-identification using appearance models and regressing motion offset, which requires costly identity annotations. In t ...

Neural information processing systems foundation2024

Reconstructing Evolving Tree Structures in Time Lapse Sequences by Enforcing Time-Consistency

Pascal Fua, Engin Türetken, Raphael Sznitman, Agata Justyna Mosinska, Przemyslaw Rafal Glowacki, Jan Kybic

We propose a novel approach to reconstructing curvilinear tree structures evolving over time, such as road networks in 2D aerial images or neural structures in 3D microscopy stacks acquired in vivo. To enforce temporal consistency, we simultaneously proces ...

2017

Estimating People Flows to Better Count Them in Crowded Scenes

Pascal Fua, Mathieu Salzmann, Weizhe Liu

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness co ...

2020

Human Detection and Segmentation via Multi-view Consensus

Pascal Fua, Mathieu Salzmann, Helge Jochen Rhodin, Isinsu Katircioglu

Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data. However, existing approaches predominantly rely on restrictive assumptions on appearance and motion. For scenes with dynamic activities and ...

IEEE2021

Take your Eyes off the Ball: Improving Ball-Tracking by Focusing on Team Play

Vitaly Henry Ablavsky, Pascal Fua, Xinchao Wang, Horesh Beny Ben Shitrit

Accurate video-based ball tracking in team sports is important for automated game analysis, and has proven very difficult because the ball is often occluded by the players. In this paper, we propose a novel approach to addressing this issue by formulating ...

Elsevier2014