Publications associées à Harris affine region detector

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

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

Incorporating Projective Geometry into Deep Learning

Michal Jan Tyszkiewicz

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

EPFL2024

Understanding generalization and robustness in modern deep learning

Maksym Andriushchenko

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Influence de la texture périurbaine sur la durabilité d’un quartier

Sophie Lufkin, Emmanuel Rey

Le présent chapitre a pour objectif d’évaluer la durabilité des neuf visions développées dans le cadre de l’atelier de projet du Prof. Emmanuel Rey pour la création d’une nouvelle polarité périurbaine à proximité immédiate du terminus de la ligne Lausanne- ...

Presses polytechniques et universitaires romandes2022

Image-based geometric digital twinning for stone masonry elements

Katrin Beyer, Bryan German Pantoja Rosero, Savvas Saloustros

We present an image-based pipeline for generating geometrical digital twins (GDTs) of stone masonry elements with detail down to the stone level. For this purpose, we acquire RGB images of the individual stones and of the wall during the construction phase ...

2022

Does jackknife scale really matter for accurate large-scale structure covariances?

Ginevra Favole

The jackknife method gives an internal covariance estimate for large-scale structure surveys and allows model-independent errors on cosmological parameters. Using the SDSS-III BOSS CMASS sample, we study how the jackknife size and number of resamplings imp ...

OXFORD UNIV PRESS2021

Gradient-based Methods for Deep Model Interpretability

Suraj Srinivas

In this dissertation, we propose gradient-based methods for characterizing model behaviour for the purposes of knowledge transfer and post-hoc model interpretation. Broadly, gradients capture the variation of some output feature of the model upon unit vari ...

EPFL2021

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Kaicheng Yu

Artificial intelligence has been an ultimate design goal since the inception of computers decades ago. Among the many attempts towards general artificial intelligence, modern machine learning successfully tackles many complex problems thanks to the progres ...

EPFL2021

Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton

The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise ...

2020

Nouvelles polarités suburbaines : comment comparer des visions projectuelles sous l'angle de leur profil de durabilité ?

Sophie Lufkin, Emmanuel Rey

La dimension holistique inhérente au concept de durabilité a induit des transformations radicales dans les pratiques architecturales et urbanistiques. La complexité croissante qui en découle entraîne la nécessité, pour les professionnels de l'espace, de di ...

2019

Detecting the Unexpected via Image Resynthesis

Pascal Fua, Mathieu Salzmann, Krishna Kanth Nakka, Krzysztof Maciej Lis

Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes ...

IEEE2019

Achieving high-resolution thermal imagery in low-contrast lake surface waters by aerial remote sensing and image registration

David Andrew Barry, Ulrich Lemmin, Daniel Sage, Abolfazl Irani Rahaghi

A two-platform measurement system for realizing airborne thermography of the Lake Surface Water Temperature (LSWT) with ~0.8 m pixel resolution (sub-pixel satellite scale) is presented. It consists of a tethered Balloon Launched Imaging and Monitoring Plat ...

2019

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

On Matching Pursuit and Coordinate Descent

Martin Jaggi, Sebastian Urban Stich, Sai Praneeth Reddy Karimireddy, Anant Raj

Two popular examples of first-order optimization methods over linear spaces are coordinate descent and matching pursuit algorithms, with their randomized variants. While the former targets the optimization by moving along coordinates, the latter considers ...

2018

A Versatile Method for Ice Particle Habit Classification Using Airborne Imaging Probe Data

Alexis Berne, Christophe Praz

A versatile method to automatically classify ice particle habit from various airborne optical array probes is presented. The classification is achieved using a multinomial logistic regression model. For each airborne probe, the model determines the particl ...

AMER GEOPHYSICAL UNION2018

Maya Codical Glyph Segmentation: A Crowdsourcing Approach

Daniel Gatica-Perez, Jean-Marc Odobez, Gülcan Can

This paper focuses on the crowd-annotation of an ancient Maya glyph dataset derived from the three ancient codices that survived up to date. More precisely, non-expert annotators are asked to segment glyph-blocks into their constituent glyph entities. As a ...

2018

An artificial neural network as a troubled-cell indicator

Jan Sickmann Hesthaven, Deep Ray

High-resolution schemes for conservation laws need to suitably limit the numerical solution near discontinuities, in order to avoid Gibbs oscillations. The solution quality and the computational cost of such schemes strongly depend on their ability to corr ...

2018

A Monolithic Approach to Downscaling Silicon Piezoresistive Sensors

Yusuf Leblebici, Mohammad Nasr Esfahani

Multi-scale integration remains the primary challenge in the fabrication of miniature piezoresistive sensors, as the co-fabrication of a silicon nanowire along with a microscale shuttle is the main architecture facilitating high-sensitivity transduction. T ...

Institute of Electrical and Electronics Engineers2017

Online Inference in Bayesian Non-Parametric Mixture Models under Small Variance Asymptotics

Sylvain Calinon, Ajay Kumar Tanwani

Adapting statistical learning models online with large scale streaming data is a challenging problem. Bayesian non-parametric mixture models provide flexibility in model selection, however, their widespread use is limited by the computational overhead of e ...

2016

Are All Pixels Equally Important? Towards Multi-Level Salient Object Detection

Gökhan Yildirim

When we look at our environment, we primarily pay attention to visually distinctive objects. We refer to these objects as visually important or salient. Our visual system dedicates most of its processing resources to analyzing these salient objects. An ana ...

EPFL2015