Publications by Aurélien Lucchi

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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 ...

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) ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Aurélien Lucchi

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Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

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

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

Flash Scanning Electron Microscopy

Efficient Scanning for EM Based Target Localization

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

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

An Optimal Policy for Target Localization with Application to Electron Microscopy

Structured Image Segmentation using Kernelized Features

A Distributed Second-Order Algorithm You Can Trust

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