Publications

You can find most articles on my Google Scholar profile.

Color Vision Deficiencies

[2021] “Understanding LMS-based Color Blindness Simulations”. Nicolas Burrus. Technical post for DaltonLens. Link.

[2021] “Review of Open Source Color Blindness Simulations”. Nicolas Burrus. Technical post for DaltonLens. Link.

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. Extended Abstract (PDF).

[2013] “3D Object Reconstruction with a Single RGB-Depth Image”. Silvia Rodríguez, Nicolas Burrus and Mohamed Abderrahim. In the Proceedings of Proceedings of VISAP. Full paper (PDF).

[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). Full paper (PDF).

[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). Buy it.

[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, Mohamed Abderrahim, Jorge Garcia Bueno and Luis Moreno. In the Proceedings of the 12th IAPR Conference on Machine Vision Applications. Full paper (PDF).

[2010] “3D Object Model Acquisition and Recognition with a Time-of-Flight camera”. Nicolas Burrus, Jorge Garcia Bueno, 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. Presentation (PDF).

[2009] “Image segmentation by a contrario simulation”. Nicolas Burrus, Thierry M. Bernard and Jean-Michel Jolion. Pattern Recognition journal. Full paper (PDF).

[2008] “Bottom-up and top-down object matching using asynchronous agents and a contrario principles”. Nicolas Burrus, Thierry M. Bernard and Jean-Michel Jolion. In the Proceedings of the 6th International Conference on Computer Vision Systems (ICVS’08). Full paper (PDF).

[2008] “Segmentation d’image par simulations a contrario”. Nicolas Burrus, Thierry M. Bernard and Jean-Michel Jolion. In the Proceedings of RFIA 2008 . Full paper (PDF).

[2006] “Smart retina as a contour-based visual interface”. Paul Nadrag, Antoine Manzanera and Nicolas Burrus. In the Proceedings of Distributed Smart Cameras Workshop (DSC’06), 2006. Full paper (PDF).

[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. Full paper (PDF).

[2005] “Détection de segments significatifs sur rétine artificielle programmable”. Nicolas Burrus Rapport de stage de master. Report (PDF).

[2004] “Visualization and White Matter Fiber Tracking with Diffusion Tensor Magnetic Resonance Images”. Nicolas Burrus. Internship report, Siemens Corporate Research (Princeton). Patent.

C++ related

[2004] “Introduction to C++ metaprogramming”. Nicolas Burrus. Tutorial for new students. Report (PDF).

[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. Full paper (PDF).

[2002] “Safe and efficient data types in C++ (Intègre)”. Nicolas Burrus. LRDE technical report. Report (PDF).

Other

[2003] “Theory of Evidence”. Nicolas Burrus and David Lesage. LRDE technical report. Report (PDF).

[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. Report (PDF).

PhD (2008)

Title: “A contrario statistical learning and efficient vision systems to detect meaningful visual events” Manuscript (PDF, in French)

Abstract: 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.

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 Acsegmentor 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.