From Nicolas Burrus Homepage

Research: Research


Publications

Computer Vision

[2013] Fusing Visual and Tactile Sensing for Manipulation of Unknown Objects

Joao Bimbo, Silvia Rodríguez-Jiménez, Hongbin Liu, Nicolas Burrus, Lakmal Senerivatne, Mohamed Abderrahim and Kaspar Althoefer
In the Proceedings of ICRA Mobile Manipulation Workshop on Interactive Perception

[2013] 3D Object Reconstruction with a Single RGB-Depth Image

Silvia Rodríguez, Nicolas Burrus and Mohamed Abderrahim
In the Proceedings of Proceedings of VISAP

[2012] A-Contrario Detection of Aerial Target Using a Time-of-Flight Camera

Silvia Rodríguez, Nicolas Burrus and Mohamed Abderrahim
In the Proceedings of the SSPD (Sensor Signal Processing for Defence

[2012] Object Pose Estimation and Tracking by Fusing Visual and Tactile Information

Joao Bimbo, Silvia Rodríguez-Jiménez, Hongbin Liu, Xiaojing Song, Nicolas Burrus, Lakmal Senerivatne, Mohamed Abderrahim and Kaspar Althoefer
In the Proceedings of MFI (Multisensor Fusion and Integration for Intelligent Systems)

[2012] Object Modeling and Detection

Nicolas Burrus
Book Chapter In Hacking the Kinect by Jeff Kramer, Nicolas Burrus, Daniel Herrera C., Florian Echtler and Matt Parker (Apress, 2012)

[2011] Textureless Object Recognition and Arm Planning for a Mobile Manipulator

Jorge Garcia Bueno, Piotr Jurewicz, Nicolas Burrus and Luis Moreno
In the Proceedings of the 53rd International Symposium ELMAR-2011, IEEE.

[2011] Object reconstruction and recognition leveraging an RGB-D camera

Nicolas Burrus and Mohamed Abderrahim and Jorge Garcia Bueno and Luis Moreno
In the Proceedings of the 12th IAPR Conference on Machine Vision Applications

[2010] Robust Pedestrian Detection using a Time-Of-Flight Camera

Jorge Garcia Bueno et al.
Talk at the 8th Robocity2030 Workshop, Madrid, Spain

[2010] 3D Object Model Acquisition and Recognition with a Time-of-Flight camera

Nicolas Burrus and Jorge Garcia Bueno and Luis Moreno and Mohamed Abderrahim
Talk at the 7th Robocity2030 Workshop, Madrid, Spain

[2009] Monocular human upper body pose estimation for sign language analysis

Nicolas Burrus and Justus Piater
Talk at the 4th Multitel Spring Workshop, Mons, Belgium

[2009] Image segmentation by a contrario simulation

Nicolas Burrus and Thierry M. Bernard and Jean-Michel Jolion
Pattern Recognition journal

[2008] Bottom-up and top-down object matching using asynchronous agents and a contrario principles

Nicolas Burrus and Thierry M. Bernard and Jean-Michel Jolion
In the Proceedings of the 6th International Conference on Computer Vision Systems (ICVS’08)

[2008] Segmentation d’image par simulations a contrario (fr)

Nicolas Burrus and Thierry M. Bernard and Jean-Michel Jolion
In the Proceedings of RFIA 2008

[2006] Smart retina as a contour-based visual interface

Paul Nadrag and Antoine Manzanera and Nicolas Burrus
In the Proceedings of Distributed Smart Cameras Workshop (DSC’06), 2006

[2006] Adaptive Vision Leveraging Digital Retinas: Extracting Meaningful Segments

Nicolas Burrus and Thierry Bernard
In the Proceedings of Advanced Concepts for Intelligent Vision Systems International Conference (ACIVS’06), 2006

[2005] Détection de segments significatifs sur rétine artificielle programmable (fr)

Nicolas Burrus
Rapport de stage de master

[2004] Visualization and White Matter Fiber Tracking with Diffusion Tensor Magnetic Resonance Images

Nicolas Burrus
Internship report, Siemens Corporate Research (Princeton)

C++ related

[2004] Introduction to C++ metaprogramming

Nicolas Burrus
Tutorial

[2003] A Static C++ Object-Oriented Programming (SCOOP) Paradigm Mixing Benefits of Traditional OOP and Generic Programming

Nicolas Burrus, Alexandre Duret-Lutz, Thierry Geraud, David Lesage and Raphaël Poss
In the Proceedings of the Workshop on Multiple Paradigm with OO Languages (MPOOL’03) Anaheim, CA, USA Oct. 2003

[2002] Safe and efficient data types in C++ (Intègre)

Nicolas Burrus
LRDE technical report

Other

[2003] Theory of Evidence

Nicolas Burrus and David Lesage
LRDE technical report

[2001] Neural Networks: Multi-Layer Perceptron and Hopfield Network

Sylvain Berlemont, Nicolas Burrus, David Lesage, Francis Maes, Jean-Baptiste Mouret, Benoît Perrot, Maxime Rey, Nicolas Tisserand, Astrid Wang
LRDE technical report

PhD

Introduction

We aim at proposing robust and efficient algorithms to detect meaningful visual events. Robustness implies, in particular, a close control of the number of false alarms made by an algorithm. Since the a contrario statistical approach has proved to match this concern, e.g. to detect geometrical primitives, we extend it to applications where the existing purely analytical framework is not adapted. By combining analytical computations with Monte-Carlo simulations or statistical learning, we applied a contrario reasoning to problems such as image segmentation into homogeneous regions, which rely on multiple features and on data-driven exploration heuristics whose mathematical properties are difficult to determine.

To satisfy the speed requirement, we also study efficient architectures. For low level vision, we experimented massive parallelism and developed a meaningful segments detection algorithm for programmable artificial retina, which operates in real-time. For high level tasks, we propose an agent-based and parallel architecture combining information priorization, parallelism between processing levels and top-down / bottom-up communications to implement “anytime” algorithms which provide results all along their execution, the most salient first. This architecture is applied to object matching and shows promising results.

Segment extraction

These principles were first applied to segment extraction in images using the computational power of the digital retinas developed at ENSTA by Thierry Bernard, resulting into an efficient, massively parallel, statistically-founded and parameterless algorithm (see acivs06).

A contrario image segmentation (acsegmentor)

The a contrario framework was then applied to a more complex problem, where exact computations are intractable: image segmentation into homogeneous regions. The resulting segmentation algorithm is called acsegmentor and can be used to filter out false alarms produced by existing algorithms. More details can be found on the project page.

Object matching

Finally, we worked on object detection within a parallel architecture (logical, not physical this time). The goal is to detect objects stored in a database (one picture per object) in new images. Thanks to a contrario learning, several similarity measures can be used in a statistically founded framework to increase detection rates. Accurate a contrario distributions can be learned with as few as 10 natural images which do not contain the database objects. Combined with an adapted, agent-based architecture, we show that this approach is suitable for an “anytime” implantation (see icvs08).

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