Publications related to Ridge detection

Enabling Uncertainty Estimation in Iterative Neural Networks

Pascal Fua, Nikita Durasov, Doruk Oner, Minh Hieu Lê

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

Curran Associates2024

Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction

Olga Fink, Ismail Nejjar, Mengjie Zhao

Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these ...

2024

Large-scale detection of marine debris in coastal areas with Sentinel-2

Devis Tuia, Marc Conrad Russwurm

Detecting and quantifying marine pollution and macroplastics is an increasingly pressing ecological issue that directly impacts ecology and human health. Here, remote sensing can provide reliable estimates of plastic pollution by regularly monitoring and d ...

2023

A 16-Channel 60µW Neural Synchrony Processor for Multi-Mode Phase-Locked Neurostimulation

Mahsa Shoaran, Uisub Shin, Cong Ding, Laxmeesha Somappa

Measuring neural oscillatory synchrony facilitates our understanding of complex brain networks and the underlying pathological states. Altering the cross-regional synchrony-as a measure of brain network connectivity-via phase-locked deep brain stimulation ...

IEEE2022

Attribute Prediction as Multiple Instance Learning

Devis Tuia, Diego Marcos Gonzalez

Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...

2022

Feedback and Common Information: Bounds and Capacity for Gaussian Networks

Erixhen Sula

Network information theory studies the communication of information in a network and considers its fundamental limits. Motivating from the extensive presence of the networks in the daily life, the thesis studies the fundamental limits of particular network ...

EPFL2021

ROXANNE Research Platform: Automate criminal investigations

Petr Motlicek, Maël Fabien, Aravind Krishnan

Criminal investigations require manual intervention of several investigators and translators. However, the amount and the diversity of the data collected raises many challenges, and cross-border investigations against organized crime can quickly impossible ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Detection and Measurement of Matrix Discontinuities in UHPFRC by Means of Distributed Fiber Optics Sensing

Eugen Brühwiler, Bartlomiej Wojciech Sawicki, Antoine Bassil

Following the significant improvement in their properties during the last decade, Distributed Fiber Optics sensing (DFOs) techniques are nowadays implemented for industrial use in the context of Structural Health Monitoring (SHM). While these techniques ha ...

2020

Real-World Gait Bout Detection Using a Wrist Sensor: An Unsupervised Real-Life Validation

Kamiar Aminian, Anisoara Ionescu, Hooman Dejnabadi, Abolfazl Soltani

Gait bouts (GB), as a prominent indication of physical activity, contain valuable fundamental information closely associated with human & x2019;s health status. Therefore, objective assessment of the GB (e.g. detection, spatio-temporal analysis) during dai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Deep Feature Factorization For Content-Based Image Retrieval And Localization

Sabine Süsstrunk, Edo Collins

State of the art content-based image retrieval algorithms owe their excellent performance to the rich semantics encoded in the deep activations of a convolutional neural network. The difference between these algorithms lies mostly in how activations are co ...

IEEE2019

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

Catchment drainage network scaling laws found experimentally in overland flow morphologies

David Andrew Barry, Andrea Rinaldo, Paolo Perona, Mohsen Cheraghi

The scaling relation between the drainage area and stream length (Hack’s law), along with exceedance probabilities of drainage area, discharge and upstream flow network length are well known for channelized fluvial regions. We report here on a laboratory e ...

2018

Blob properties in full-turbulence simulations of the TCV scrape-off layer

Ivo Furno, Paolo Ricci, Benoît Labit, Fabio Avino, Federico Nespoli, Federico David Halpern, Fabio Riva

To investigate blob properties in the tokamak scrape-off layer (SOL), we perform dedicated numerical nonlinear simulations of plasma turbulence in the SOL of a TCV discharge using the Global Braginskii Solver code. A blob detection technique is used for th ...

2017

Stereo-vision three-dimensional reconstruction of curvilinear structures imaged with a TEM

Pascal Fua, Cécile Hébert, Marco Cantoni, Emad Oveisi, Guillaume Lucas, Antoine Letouzey, Sandro Ivo Sebastiano De Zanet

Deriving accurate three-dimensional (3-D) structural information of materials at the nanometre level is often crucial for understanding their properties. Tomography in transmission electron microscopy (TEM) is a powerful technique that provides such inform ...

Elsevier2017

A graph signal filtering-based approach for detection of different edge types on airborne lidar data

Elif Vural, Eda Bayram

Airborne Laser Scanning is a well-known remote sensing technology, which provides a dense and highly accurate, yet unorganized point cloud of earth surface. During the last decade, extracting information from the data generated by airborne LiDAR systems ha ...

2017

Improved Directional Derivatives for Modifier-Adaptation Schemes

Dominique Bonvin, Timm Faulwasser, Alejandro Gabriel Marchetti, Martand Singhal

The modifier-adaptation methodology enables real-time optimization (RTO) of plant operation in the presence of considerable plant-model mismatch. It requires the estimation of plant gradients. Obtaining these gradients is expensive as it involves potential ...

2017

Extremal behavior of squared Bessel processes attracted by the Brown-Resnick process

Sebastian Engelke

The convergence of properly time-scaled and normalized maxima of independent standard Brownian motions to the Brown-Resnick process is well-known in the literature. In this paper, we study the extremal functional behavior of non-Gaussian processes, namely ...

Elsevier Science Bv2015

A Deeper Look at Dataset Bias

Barbara Caputo, Tatiana Tommasi, Novi Patricia

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid deve ...

Springer Int Publishing Ag2015

Leveraging Colour Segmentation for Upper-Body Detection

Jean-Marc Odobez

This paper presents an upper-body detection algorithm that extends classical shape-based detectors through the use of additional semantic colour segmentation cues. More precisely, candidate upper-body image patches produced by a base detector are soft-segm ...

2014

Coupled Structure-from-Motion and 3D Symmetry Detection for Urban Facades

Mark Pauly, Duygu Ceylan

Repeated structures are ubiquitous in urban facades. Such repetitions lead to ambiguity in establishing correspondences across sets of unordered images. A decoupled structure-from-motion reconstruction followed by symmetry detection often produces errors: ...

2014