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Publications associées (30)

ActiveAx(ADD): Toward non-parametric and orientationally invariant axon diameter distribution mapping using PGSE

Jean-Philippe Thiran, David Paul Roger Romascano, Jonathan Rafael Patino Lopez, Alessandro Daducci, Muhamed Barakovic, Tim Bjørn Dyrby

Purpose Non-invasive axon diameter distribution (ADD) mapping using diffusion MRI is an ill-posed problem. Current ADD mapping methods require knowledge of axon orientation before performing the acquisition. Instead, ActiveAx uses a 3D sampling scheme to e ...

Wiley2020

Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks

Daniel Thalmann, Hui Liang

In this paper, we present a novel method for real-time 3D hand pose estimation from single depth images using 3D Convolutional Neural Networks (CNNs). Image-based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to th ...

IEEE COMPUTER SOC2019

Detection of Age-Induced Makeup Attacks on Face Recognition Systems Using Multi-Layer Deep Features

Sébastien Marcel, Zohreh Mostaani

Makeup is a simple and easy instrument that can alter the appearance of a person’s face, and hence, create a presentation attack on face recognition (FR) systems. These attacks, especially the ones mimicking ageing, are difficult to detect due to their clo ...

2020

Neural network training for cross-protocol radiomic feature standardization in computed tomography

Adrien Raphaël Depeursinge

Radiomics has shown promising results in several medical studies, yet it suffers from a limited discrimination and informative capability as well as a high variation and correlation with the tomographic scanner types, pixel spacing, acquisition protocol, a ...

SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS2019

A Performance Evaluation of Local Features for Image-Based 3D Reconstruction

Pascal Fua, Bin Fan, Bin Fan, Zheng Wang, Xiao Wang

This paper performs a comprehensive and comparative evaluation of the state of the art local features for the task of image based 3D reconstruction. The evaluated local features cover the recently developed ones by using powerful machine learning technique ...

2019

A Personalized Approach to Improve Walking Detection in Real-Life Settings: Application to Children with Cerebral Palsy

Kamiar Aminian, Anisoara Ionescu, Lena Carcreff, Stéphane Armand

Although many methods have been developed to detect walking by using body-worn inertial sensors, their performances decline when gait patterns become abnormal, as seen in children with cerebral palsy (CP). The aim of this study was to evaluate if fine-tuni ...

2019

Beyond Cartesian Representations for Local Descriptors

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

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

Data-Interpretation Methodologies for Practical Asset-Management

Ian Smith, Yves Sylvain Gilles Reuland, Sai Ganesh Sarvotham Pai

Monitoring and interpreting structural response using structural-identification methodologies improves understanding of civil-infrastructure behavior. New sensing devices and inexpensive computation has made model-based data interpretation feasible in engi ...

2019

Angular Accuracy of Steerable Feature Detectors

Michaël Unser, Arash Amini, Zsuzsanna Püspöki, Julien René Pierre Fageot, John Paul Ward

The detection of landmarks or patterns is of interest for extracting features in biological images. Hence, algorithms for finding these keypoints have been extensively investigated in the literature, and their localization and detection properties are well ...

SIAM PUBLICATIONS2019

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Edo Collins

With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia. Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...

EPFL2019

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