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Pascal Fua

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Publications associées (322)

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

Stochastic Exploration of Ambiguities for Non-Rigid Shape Recovery

Pascal Fua

Recovering the 3D shape of deformable surfaces from single images is known to be a highly ambiguous problem because many different shapes may have very similar projections. This is commonly addressed by restricting the set of possible shapes to linear comb ...

Institute of Electrical and Electronics Engineers2013

TPAMI CVPR Special Section

Pascal Fua

Institute of Electrical and Electronics Engineers2013

Active Testing Search for Point Cloud Matching

Pascal Fua, Jan Kybic, Raphael Sznitman, Miguel Amável Dos Santos Pinheiro

We present a general approach for solving the point-cloud matching problem for the case of mildly nonlinear transformations. Our method quickly finds a coarse approximation of the solution by exploring a reduced set of partial matches using an approach to ...

2013

Supervised Feature Learning for Curvilinear Structure Segmentation

Pascal Fua, Vincent Lepetit, Roberto Rigamonti, Carlos Joaquin Becker

Springer2013

Fast Object Detection with Entropy-Driven Evaluation

Pascal Fua, François Fleuret, Raphael Sznitman, Carlos Joaquin Becker

Cascade-style approaches to implementing ensemble classifiers can deliver significant speed-ups at test time. While highly effective, they remain challenging to tune and their overall performance depends on the availability of large validation sets to esti ...

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

BRIEF: Computing a Local Binary Descriptor Very Fast

Pascal Fua, Vincent Lepetit, Mustafa Özuysal, Michael Calonder, Christoph Strecha, Tomasz Piotr Trzcinski

Binary descriptors are becoming increasingly popular as a means to compare feature points very fast and while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm ...

Institute of Electrical and Electronics Engineers2012

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

LDAHash: Improved Matching with Smaller Descriptors

Pascal Fua, Christoph Strecha

SIFT-like local feature descriptors are ubiquitously employed in computer vision applications such as content-based retrieval, video analysis, copy detection, object recognition, photo tourism, and 3D reconstruction. Feature descriptors can be designed to ...

Institute of Electrical and Electronics Engineers2012

Hybrid Algorithms for the Minimum-Weight Rooted Arborescence Problem

Pascal Fua, Engin Türetken

2012

Data-Driven Visual Tracking in Retinal Microsurgery

Pascal Fua, Raphael Sznitman, Karim Ali

In the context of retinal microsurgery, visual tracking of instruments is a key component of robotics assistance. The difficulty of the task and major reason why most existing strategies fail on {\it in-vivo} image sequences lies in the fact that complex a ...

2012

Learning Image Descriptors with the Boosting-Trick

Pascal Fua, Vincent Lepetit, Tomasz Piotr Trzcinski, Christos Marios Christoudias

In this paper we apply boosting to learn complex non-linear local visual feature representations, drawing inspiration from its successful application to visual object detection. The main goal of local feature descriptors is to distinctively repre- sent a s ...

2012

Monocular 3D Reconstruction of Locally Textured Surfaces

Pascal Fua, Mathieu Salzmann, Aydin Varol, Appu Shaji

Most recent approaches to monocular non-rigid 3D shape recovery rely on exploiting point correspondences and work best when the whole surface is well-textured. The alternative is to rely either on contours or shading information, which has only been demons ...

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

Gradient Response Maps for Real-Time Detection of Textureless Objects

Pascal Fua, Slobodan Ilic, Vincent Lepetit

We present a method for real-time 3D object instance detection that does not require a time-consuming training stage, and can handle untextured objects. At its core, our approach is a novel image representation for template matching designed to be robust t ...

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

Robust Non-Rigid Registration of 2D and 3D Graphs

Pascal Fua, Jan Kybic, Przemyslaw Rafal Glowacki

We present a new approach to matching graphs embedded in R2 or R3. Unlike earlier methods, our approach does not rely on the similarity of local appearance features, does not require an initial alignment, can handle partial matches, and can cope with non-l ...

2012

A Real-Time Deformable Detector

David Hasler, Pascal Fua, François Fleuret, Karim Ali

We propose a new learning strategy for object detection. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform base ...

Institute of Electrical and Electronics Engineers2012

Automated Reconstruction of Tree Structures using Path Classifiers and Mixed Integer Programming

Pascal Fua, Engin Türetken, Fethallah Benmansour

Although tracing linear structures in 2D images and 3D image stacks has received much attention over the years, full automation remains elusive. In this paper, we formulate the delineation problem as one of solving a Quadratic Mixed Integer Program (Q-MIP) ...

2012

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