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Deep(ly) Learning the Depth

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Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

Structured Prediction of 3D Human Pose with Deep Neural Networks

Pascal Fua, Mathieu Salzmann, Isinsu Katircioglu, Vincent Lepetit, Bugra Tekin

Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a ...

2016

Learning from Examples in Optical Imaging

Michaël Unser, Demetri Psaltis, Cédric René Jean Vonesch, Ulugbek Kamilov

Imaging systems can be designed using examples and methods similar to the techniques used in deep learning. We describe experimental results demonstrating optical tomography based on the learning approach. ...

OSA2016

Non-parametric Blur Map Regression for Depth of Field Extension

Sabine Süsstrunk

Real camera systems have a limited depth of field (DOF) which may cause an image to be degraded due to visible misfocus or too shallow DOF. In this paper, we present a blind deblurring pipeline able to restore such images by slightly extending their DOF an ...

Institute of Electrical and Electronics Engineers2016

Camera-based estimation of student's attention in class

Mirko Raca

Two essential elements of classroom lecturing are the teacher and the students. This human core can easily be lost in the overwhelming list of technological supplements aimed at improving the teaching/learning experience. We start from the question of whet ...

EPFL2015

Models of Reward-Modulated Spike-Timing-Dependent Plasticity

Nicolas Frémaux

How do animals learn to repeat behaviors that lead to the obtention of food or other “rewarding” objects? As a biologically plausible paradigm for learning in spiking neural networks, spike-timing dependent plasticity (STDP) has been shown to perform well ...

EPFL2013

From N to N+1: Multiclass Transfer Incremental Learning

Barbara Caputo, Ilja Kuzborskij

Since the seminal work of Thrun [16], the learning to learn paradigm has been defined as the ability of an agent to improve its performance at each task with experience, with the number of tasks. Within the object categorization domain, the visual learning ...

2013

Iterative Learning of Feed-Forward Corrections for High-Performance Tracking

We revisit a recently developed iterative learning algorithm that enables systems to learn from a repeated operation with the goal of achieving high tracking performance of a given trajectory. The learning scheme is based on a coarse dynamics model of the ...

2012

Distributed Successive Refinement of Multiview Images Using Broadcast Advantage

Martin Vetterli, Guillermo Barrenetxea Kobas, Zichong Chen

In environmental monitoring applications, having multiple cameras focus on common scenery increases robustness of the system. To save energy based on user demand, successive refinement image coding is important, as it allows us to progressively request bet ...

Institute of Electrical and Electronics Engineers2012

A Unified Calibration Approach For Generic Cameras

The classic perspective projection is mostly used when calibrating a camera. Although this approach is fairly developed and often suitable, it is not necessarily adequate to model any camera system like fish-eyes or catadioptrics. The perspective projectio ...

2010