Publications by Aurélien Lucchi

A Distributed Second-Order Algorithm You Can Trust

Thomas Hofmann, Aurélien Lucchi, Matilde Gargiani, Martin Jaggi

Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate the field, second-order methods are nevertheless attractive as they potenti ...

2018

Learning Aerial Image Segmentation from Online Maps

Thomas Hofmann, Aurélien Lucchi, Martin Jaggi, Jan Dirk Wegner

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) ...

Institute of Electrical and Electronics Engineers2017

Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

Thomas Hofmann, Aurélien Lucchi, Simon Müller, Martin Jaggi

2017

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

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

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

Efficient scanning for em based target localization

Pascal Fua, Graham Knott, Raphael Sznitman, Aurélien Lucchi

We presented an approach for speeding-up image acquisition when tasked with localizing specific structures in FIB-SEM imagery. It exploits the fact that low-quality images can be acquired faster than higher-quality ones and yet be sufficient for inference ...

2014

Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images

Aurélien Lucchi

Segmenting images is a significant challenge that has drawn a lot of attention from different fields of artificial intelligence and has many practical applications. One such challenge addressed in this thesis is the segmentation of electron microscope (EM) ...

EPFL2013

An Optimal Policy for Target Localization with Application to Electron Microscopy

Pascal Fua, Raphael Sznitman, Aurélien Lucchi

This paper considers the task of finding a target location by making a limited number of sequential observations. Each observation results from evaluating an imperfect classifier of a chosen cost and accuracy on an interval of chosen length and position. W ...

2013

Flash Scanning Electron Microscopy

Pascal Fua, Marco Cantoni, Graham Knott, Raphael Sznitman, Aurélien Lucchi

Scanning Electron Microscopy (SEM) is an invaluable tool for biologists and neuroscientists to study brain structure at the intra- cellular level. While able to image tissue samples with up to 5nm isotropic resolution, image acquisition is prohibitively sl ...

2013

Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

Pascal Fua, Aurélien Lucchi, Yunpeng Li

We propose a working set based approximate subgradient descent algorithm to minimize the margin-sensitive hinge loss arising from the soft constraints in max-margin learning frameworks, such as the structured SVM. We focus on the setting of general graphic ...

2013

Structured Image Segmentation using Kernelized Features

Pascal Fua, Kevin Smith, Aurélien Lucchi, Yunpeng Li

Most state-of-the-art approaches to image segmentation formulate the problem using Conditional Random Fields. These models typically include a unary term and a pairwise term, whose parameters must be carefully chosen for optimal performance. Recently, stru ...

2012

Efficient Scanning for EM Based Target Localization

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

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

Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features

Pascal Fua, Graham Knott, Kevin Smith, Radhakrishna Achanta, Aurélien Lucchi

It is becoming increasingly clear that mitochondria play an important role in neural function. Recent studies show mitochondrial morphology to be crucial to cellular physiology and synaptic function and a link between mitochondrial defects and neuro-degene ...

Institute of Electrical and Electronics Engineers2012

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

Pascal Fua, Sabine Süsstrunk, Kevin Smith, Radhakrishna Achanta, 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

An empirical evaluation of touch and tangible interfaces for tabletop displays

Guillaume Zufferey, Pierre Dillenbourg, Patrick Jermann, Aurélien Lucchi

Tabletop systems have become quite popular in recent years, during which there was considerable enthusiasm for the development of new interfaces. In this paper, we establish a comparison between touch and tangible interfaces. We set up an experiment involv ...

2010

A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images

Pascal Fua, Vincent Lepetit, Kevin Smith, Radhakrishna Achanta, Aurélien Lucchi

While there has been substantial progress in segmenting natural im-ages, state-of-the-art methods that perform well in such tasks unfortunately tend to underperform when confronted with the different challenges posed by electron microscope (EM) data. For e ...

Springer2010

TinkerSheets: Using Paper Forms to Control and Visualize Tangible Simulations

Guillaume Zufferey, Pierre Dillenbourg, Patrick Jermann, Aurélien Lucchi

This paper describes TinkerSheets, a paper-based interface to tangible simulations. The proposed interface combines the advantages of form-based input and paper. Form-based input allows to set an arbitrary number of parameters. Using paper as a medium for ...

2009