Where PhDs and companies meet
Menu
Login

Already registered?

New user?

EventSpike - Asynchronous computer vision from event cameras

ABG-128045 Thesis topic
2025-01-21 Public funding alone (i.e. government, region, European, international organization research grant)
Université de Lille
Lille - Les Hauts de France - France
EventSpike - Asynchronous computer vision from event cameras
  • Computer science
SNN, vision par ordinateur, computer vision, réseaux impulsionnels, caméra evenementielle

Topic description

The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France and the PR team from the MIS Laboratory, Amiens France are looking to recruit a joint PhD student starting in October 2025 on the following subject: EventSpike - Asynchronous computer vision from event cameras

Abstract: Video analysis is one of the fundamental tasks in computer vision. The dominant approach is based on deep neural networks applied to RGB images. These models have disadvantages such as: a) the need of large quantities of annotated data, which requires significant human work; b) the significant computational and therefore energy cost of these approaches; and c) redundancy in terms of visual information between two successive images. Spiking neural networks can offer a solution to these problems, through the use of unsupervised learning rules inspired by biological learning and the possibility of implementing them on ultra-low energy hardware components. . Event cameras that only communicate changes in light intensity are positioned as an alternative for capturing a scene when efficient processing on hardware with low computing capabilities is required. The objective of this thesis is to offer a joint response by proposing weakly supervised learning methods based on spiking learning mechanisms which will directly exploit the flow of impulses generated by an event camera. We are targeting applications around autonomous driving, such as the detection of moving vehicles or the recognition of information conveyed by large displays along the road infrastructure.

The objective of this thesis is to develop new models of spiking neural networks (SNN) capable of directly processing visual information in the form of spike trains. The proposed models must be validated experimentally on dynamic vision databases, following standard protocols and best practices.

The candidate will be funded for 3 years; he/she is expected to defend his/her thesis and graduate by the end of the contract. The monthly gross salary is around 2000€, including benefits (health insurance, retirement fund, and paid vacations).

We look forward to receiving your application until 12.04.2024

The position is located in Lille, France. With over 110 000 students, the metropolitan area of Lille is one France's top education student cities. The European Doctoral College Lille Nord-Pas de Calais is headquartered in Lille Metropole and includes 3,000 PhD Doctorate students supported by university research laboratories. Lille has a convenient location in the European high-speed rail network. It lies on the Eurostar line to London (1:20 hour journey). The French TGV network also puts it only 1 hour from Paris, 35 mn from Brussels, and a short trips to other major centres in France such as Paris, Marseille and Lyon.

https://www.cristal.univ-lille.fr/FOX/
https://www.mis.u-picardie.fr/index.php/equipe/perception-robotique

Starting date

2025-10-01

Funding category

Public funding alone (i.e. government, region, European, international organization research grant)

Funding further details

Presentation of host institution and host laboratory

Université de Lille

The FOX research group is part of the CRIStAL laboratory (University of Lille, CNRS), located in Lille, France. We focus on video analysis for human behavior understanding. Specifically, we develop spatio-temporal models of motions for tasks such as abnormal event detection, emotion recognition, and face alignment. We are also involved in IRCICA (CNRS), a research institute promoting multidisciplanary research. At IRCICA, we collaborate with computer scientists and experts in electronics engineering to create new models of neural networks that can be implemented on low-power hardware architectures. Recently, we designed state-of-the-art models for image recognition with single and multi-layer unsupervised spiking neural networks. We were among the first to succesfully apply unsupervised SNNs on modern datasets of computer vision. We also developed our own SNN simulator to support experiments with SNN on computer vision problems.

Our work is published in major journals (Pattern Recognition, IEEE Trans. on Affective Computing) and conferences (NeurIPS, WACV, IJCNN) in the field.

The PR (Robotic Perception) team has skills in mobile robotics (perception), 3D reconstruction and unconventional vision. The PR team is piloting the e-Cathedral program (2015-2025) and is currently involved in four other projects which focus on event vision: the EVENTO project (2021-2024) co-financed by AID, the ANR CERBERE project ( 2022-2025) carried out by the LITIS laboratory of the University of Rouen, the ANR DEVIN project (2024-2028) carried by the I3S laboratory of the University of Côte d'Azur and the ANR-FWF project (France-Austria) (2024-2028) carried by the UPJV and by TU Graz. The PR team wishes to further strengthen this area of research and improve its expertise in AI through collaboration with the CRIStAL laboratory.

https://www.cristal.univ-lille.fr/FOX/
https://www.mis.u-picardie.fr/index.php/equipe/perception-robotique

PhD title

Doctorat en Informatique

Country where you obtained your PhD

France

Institution awarding doctoral degree

Université de Lille

Graduate school

Sciences pour l'ingénieur (SPI)

Candidate's profile

Candidates must hold a Master degree (or an equivalent degree) in Computer Science, Statistics, Applied Mathematics or a related field. Experience in one or more of the following is a plus:

 • image processing, computer vision;

 • machine learning;

 • bio-inspired computing;

 • research methodology (literature review, experimentation…).

 Candidates should have the following skills:

 • good proficiency in English, both spoken and written;

 • scientific writing;

 • programming (experience in C++ is a plus, but not mandatory).
 

2025-04-11
Partager via
Apply
Close

Vous avez déjà un compte ?

Nouvel utilisateur ?