Publications by Carlos Joaquin Becker

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images

Pascal Fua, Mathieu Salzmann, Carlos Joaquin Becker, Róger Bermúdez Chacón, Okan Altingövde

We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences—motifs that are visually similar across different acquisitions—to infer changes on the parame ...

Institute of Electrical and Electronics Engineers2020

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Pascal Fua, Engin Türetken, Xinchao Wang, Carlos Joaquin Becker

We propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypothe ...

Institute of Electrical and Electronics Engineers2017

Scalable Unsupervised Domain Adaptation for Electron Microscopy

Pascal Fua, Mathieu Salzmann, Carlos Joaquin Becker, Róger Bermúdez Chacón

While Machine Learning algorithms are key to automating organelle segmentation in large EM stacks, they require annotated data, which is hard to come by in sufficient quantities. Furthermore, images acquired from one part of the brain are not always repres ...

2016

Towards Scalable Segmentation of 3D Image Stacks

Carlos Joaquin Becker

Imaging modalities such as Electron Microscopy (EM) and Light Microscopy (LM) can now deliver high-quality, high-resolution image stacks of neural structures. Though these imaging modalities can be used to analyze a variety of components that are critical ...

EPFL2016

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

Domain Adaptation for Microscopy Imaging

Pascal Fua, Carlos Joaquin Becker, Christos Marios Christoudias

Electron and Light Microscopy imaging can now deliver high-quality image stacks of neural structures. However, the amount of human annotation effort required to analyze them remains a major bottleneck. While Machine Learning algorithms can be used to help ...

Institute of Electrical and Electronics Engineers2015

Fast Part-Based Classification for Instrument Detection in Minimally Invasive Surgery

Pascal Fua, Raphael Sznitman, Carlos Joaquin Becker

Automatic visual detection of instruments in minimally invasive surgery (MIS) can significantly augment the procedure experience for operating clinicians. In this paper, we present a novel technique for detecting surgical instruments by constructing a robu ...

Springer2014

Detecting Irregular Curvilinear Structures in Gray Scale and Color Imagery using Multi-Directional Oriented Flux

Pascal Fua, Engin Türetken, Fethallah Benmansour, Carlos Joaquin Becker, Przemyslaw Rafal Glowacki

2013

Non-Linear Domain Adaptation with Boosting

Pascal Fua, Carlos Joaquin Becker, Christos Marios Christoudias

A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can gen ...

2013

Learning Context Cues for Synapse Segmentation

Pascal Fua, Graham Knott, Carlos Joaquin Becker, Karim Ali

We present a new approach for the automated segmentation of synapses in image stacks acquired by Electron Microscopy (EM) that relies on image features specifically designed to take spatial context into account. These features are used to train a classifie ...

Institute of Electrical and Electronics Engineers2013