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Pascal Fua

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Publications associées (319)

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Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization with Geometric Priors

Pascal Fua, Engin Türetken, Germán González Serrano

We present a novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, ours builds a set of candidate trees over many different subsets ...

Humana Press2011

Learning Context Cues for Synapse Segmentation in EM Volumes

Pascal Fua, Graham Knott, Carlos Joaquin Becker, Karim Ali

We present a new approach for the automated segmentation of excitatory synapses in image stacks acquired by electron microscopy. We rely on a large set of image features specifically designed to take spatial context into account and train a classifier that ...

2012

Masksembles for Uncertainty Estimation

Pascal Fua, Timur Bagautdinov, Pierre Bruno Baqué, Nikita Durasov

Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being one of the best methods for generating uncertainty estimates but are very expe ...

2021

The Accuracy of Automatic Photogrammetric Techniques on Ultra-light UAV Imagery

François Gervaix, Pascal Fua, Dario Floreano, Jean-Christophe Zufferey, Antoine Beyeler, Christoph Strecha, Olivier Küng

This paper presents an affordable, fully automated and accurate mapping solutions based on ultra-light UAV imagery. Several datasets are analysed and their accuracy is estimated. We show that the accuracy highly depends on the ground resolution (flying hei ...

2011

Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis

Pascal Fua, Jiancheng Yang, Pamuditha Udaranga Wickramasinghe

The human annotations are imperfect, especially when produced by junior practitioners. Multi-expert consensus is usually regarded as golden standard, while this annotation protocol is too expensive to implement in many real-world projects. In this study, w ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate

Pascal Fua, Mateusz Kozinski, Doruk Oner, Leonardo Citraro

Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data i ...

Institute of Electrical and Electronics Engineers2022

Making Action Recognition Robust to Occlusions and Viewpoint Changes

Pascal Fua, Mustafa Özuysal

Most state-of-the-art approaches to action recognition rely on global representations either by concatenating local information in a long descriptor vector or by computing a single location independent histogram. This limits their performance in presence o ...

2010

Learning Active Learning from Data

Pascal Fua, Raphael Sznitman, Ksenia Konyushkova

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection proced ...

2017

Tracking Multiple People under Global Appearance Constraints

Pascal Fua, François Fleuret, Horesh Beny Ben Shitrit

In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a convex global optimization problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effect ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

A Latent Implicit 3D Shape Model for Multiple Levels of Detail

Pascal Fua, Benoît Alain René Guillard

Implicit neural representations map a shape-specific latent code and a 3D coordinate to its corresponding signed distance (SDF) value. However, this approach only offers a single level of detail. Emulating low levels of detail can be achieved with shallow ...

Springer Nature (Switzerland)2025

Learning to Assign Orientations to Feature Points

Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Yannick Verdie

We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction ...

2016

Beyond the Pixel-Wise Loss for Topology-Aware Delineation

Pascal Fua, Pablo Marquez Neila, Mateusz Kozinski, Agata Justyna Mosinska

Delineation of curvilinear structures is an important problem in Computer Vision with multiple practical applications. With the advent of Deep Learning, many current approaches on automatic delineation have focused on finding more powerful deep architectur ...

2018

Refining Mitochondria Segmentation in Electron Microscopy Imagery with Active Surfaces

Pascal Fua, Anne Alison Jorstad

We present an active surface-based method for refining the boundary surfaces of mitochondria segmentation data. We exploit the fact that mitochondria have thick dark membranes, so referencing the image data at the inner membrane can help drive a more accur ...

Springer2014

Real-time vehicle tracking for driving assistance

Pascal Fua, Patrick Merryl Marlon Schönmann

Detecting car taillights at night is a task which can nowadays be accomplished very fast on cheap hardware. We rely on such detections to build a vision-based system that, coupling them in a rule-based fashion, is able to detect and track vehicles. This al ...

2011

Caenorhabditis elegant segmentation using texture-based models for motility phenotyping

Pascal Fua, Raphael Sznitman

With widening interests in using model organisms for reverse genetic approaches and biomimmetic micro-robotics, motility phenotyping of the nematode Caenorhabditis elegans is expanding across a growing array of locomotive environments. One ongoing bottlene ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Efficient Scanning for EM Based Target Localization

Natasa Pjescic-Emedi, Pascal Fua, Graham Knott, Raphael Sznitman, Aurélien Lucchi

For biologists studying the morphology of cells, Electron Microscopy (EM) is the method of choice with its {\it nm} resolution, across increasingly larger volumes. However, the time necessary to acquire such image series is long and often limits the amount ...

2012

SLIC Superpixels Compared to State-of-the-art Superpixel Methods

Pascal Fua, Sabine Süsstrunk, Radhakrishna Achanta, Kevin Smith, Aurélien Lucchi, Appu Shaji

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically c ...

Institute of Electrical and Electronics Engineers2012

Worldwide Pose Estimation Using 3D Point Clouds

Pascal Fua, Yunpeng Li

We address the problem of determining where a photo was taken by estimating a full 6-DOF-plus-intrincs camera pose with respect to a large geo-registered 3D point cloud, bringing together research on image localization, landmark recognition, and 3D pose es ...

2012

Principled Parallel Mean-Field Inference for Discrete Random Fields

Pascal Fua, François Fleuret, Timur Bagautdinov, Pierre Bruno Baqué

Mean-field variational inference is one of the most popular approaches to inference in discrete random fields. Standard mean-field optimization is based on coordinate descent and in many situations can be impractical. Thus, in practice, various parallel te ...

2016

Method, system, and device for learned invariant feature transform for computer images

Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Eduard Trulls Fortuny

A method for training a feature detector of an image processing device, including the steps of detecting features in the image to generate a score map, computing a center of mass on the score map to generate a location, extracting a patch from the image at ...

2018