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Learning to Represent and Reconstruct 3D Deformable Objects

Related publications (75)

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Efficient Sample Mining for Object Detection

François Fleuret, Olivier Canévet

We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have devel ...

2014

Object Classification and Detection in High Dimensional Feature Space

Charles Dubout

Object classification and detection aim at recognizing and localizing objects in real-world images. They are fundamental computer vision problems and a prerequisite for full scene understanding. Their difficulty lies in the large number of possible object ...

Programme doctoral en Informatique, Communications et Information2013

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

Resolving Ambiguities in Monocular 3D Reconstruction of Deformable Surfaces

Aydin Varol

In this thesis, we focus on the problem of recovering 3D shapes of deformable surfaces from a single camera. This problem is known to be ill-posed as for a given 2D input image there exist many 3D shapes that give visually identical projections. We present ...

EPFL2012

Learning Separable Filters

Pascal Fua, Vincent Lepetit, Roberto Rigamonti

While learned image features can achieve great accuracy on different Computer Vision problems, their use in real-world situations is still very limited as their extraction is typically time-consuming. We therefore propose a method to learn image features t ...

2012

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

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

A Constrained Latent Variable Model

Pascal Fua, Mathieu Salzmann, Raquel Urtasun, Aydin Varol

Latent variable models provide valuable compact representations for learning and inference in many computer vision tasks. However, most existing models cannot directly encode prior knowledge about the specific problem at hand. In this paper, we introduce a ...

Ieee2012

Object Duplicate Detection

Péter Vajda

With the technological evolution of digital acquisition and storage technologies, millions of images and video sequences are captured every day and shared in online services. One way of exploring this huge volume of images and videos is through searching a ...

EPFL2011

Comparing machines and humans on a visual categorization test

François Fleuret, Charles Dubout, Ting Li

Automated scene interpretation has benefited from advances in machine learning, and restricted tasks, such as face detection, have been solved with sufficient accuracy for restricted settings. However, the performance of machines in providing rich semantic ...

2011

Vision-Based Navigation in Autonomous Close Proximity Operations Using Neural Networks

Seyed Mohammad Khansari Zadeh

Tight unmanned aerial vehicle (UAV) autonomous missions such as formation flight (FF) and aerial refueling (AR) require an active controller that works in conjunction with a precise sensor that is able to identify an in-front aircraft and to estimate its r ...

Institute of Electrical and Electronics Engineers2011