Publications related to Learning and recovering 3D surface deformations

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

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

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

Single-Shot Imaging with Higher-Dimensional Encoding Using Magnetic Field Monitoring and Concomitant Field Correction

Daniel Gallichan

PurposePatLoc (Parallel Imaging Technique using Localized Gradients) accelerates imaging and introduces a resolution variation across the field-of-view. Higher-dimensional encoding employs more spatial encoding magnetic fields (SEMs) than the corresponding ...

Wiley-Blackwell2015

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

Projection onto the Manifold of Elongated Structures for Accurate Extraction

Pascal Fua, Vincent Lepetit, Amos Sironi

Detection of elongated structures in 2D images and 3D image stacks is a critical prerequisite in many applications and Machine Learning-based approaches have recently been shown to deliver superior performance. However, these methods essentially classify i ...

2015

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

On Rendering Synthetic Images for Training an Object Detector

Pascal Fua, Vincent Lepetit, Artem Rozantsev

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of ...

Elsevier2015

Joint image reconstruction and segmentation using the Potts model

Michaël Unser, Martin Kurt Storath

We propose a new algorithmic approach to the non-smooth and non-convex Potts problem (also called piecewise-constant Mumford-Shah problem) for inverse imaging problems. We derive a suitable splitting into specific subproblems that can all be solved efficie ...

Institute of Physics2015

Saliency-based Representations and Multi-component Classifiers for Visual Scene Recognition

Marco Fornoni

Visual scene recognition deals with the problem of automatically recognizing the high-level semantic concept describing a given image as a whole, such as the environment in which the scene is occurring (e.g. a mountain), or the event that is taking place ( ...

École Polytechnique Fédérale de Lausanne (EPFL)2014

Saliency-based representations and multi-component classifiers for visual scene recognition

Marco Fornoni

Visual scene recognition deals with the problem of automatically recognizing the high-level semantic concept describing a given image as a whole, such as the environment in which the scene is occurring (e.g. a mountain), or the event that is taking place ( ...

EPFL2014

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

Frank Grégoire Jean de Morsier

The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety of existing data sources, the satellites continuously observing the surface of the Earth ...

EPFL2014

Learning and recovering 3D surface deformations

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

Single-Shot Imaging with Higher-Dimensional Encoding Using Magnetic Field Monitoring and Concomitant Field Correction

Domain Adaptation for Microscopy Imaging

Projection onto the Manifold of Elongated Structures for Accurate Extraction

Sparsistency of $\ell_1$-Regularized $M$-Estimators

On Rendering Synthetic Images for Training an Object Detector

Joint image reconstruction and segmentation using the Potts model

Saliency-based Representations and Multi-component Classifiers for Visual Scene Recognition

Saliency-based representations and multi-component classifiers for visual scene recognition

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

Saliency-based Representations and Multi-component Classifiers for Visual Scene Recognition

Saliency-based representations and multi-component classifiers for visual scene recognition

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Single-Shot Imaging with Higher-Dimensional Encoding Using Magnetic Field Monitoring and Concomitant Field Correction

Domain Adaptation for Microscopy Imaging

Projection onto the Manifold of Elongated Structures for Accurate Extraction

On Rendering Synthetic Images for Training an Object Detector

Joint image reconstruction and segmentation using the Potts model

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

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