02/06/2020
The Human Resources Strategy for Researchers

PhD contract in the field of Computer science and Mathematics financed during 3 years by the University Clermont Auvergne

This job offer has expired


  • ORGANISATION/COMPANY
    Université Clermont Auvergne
  • RESEARCH FIELD
    Computer science
    Mathematics
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    28/06/2020 00:00 - Europe/Brussels
  • LOCATION
    France › AUBIERE
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35 H
  • OFFER STARTING DATE
    01/10/2020
  • REFERENCE NUMBER
    UCA/ANR/012
  • IS THE JOB RELATED TO STAFF POSITION WITHIN A RESEARCH INFRASTRUCTURE?
    Yes

Subject: Automatic objects detection and counting in videos of a production

system

Supervisor: Chafik Samir

Laboratory: LIMOS UMR 6158

Email and phone: chafik.samir@uca.fr

Co-advisor(s):

Abstract (up to 10 lines):

Detecting and classifying multiple forms of objects in a video stream is becoming a key step in

different artificial systems. For example, automatic supervision and detection by learning

methods are increasingly used for decision support in industrial, agricultural, food, etc.

production systems. The main challenge consists in finding a method that would be able to

distinguish the classes (categories) of objects while detecting them properly. In this project we

propose to develop a unified framework that combines the two techniques using Bayesian

modeling. This will be made possible witha stochastic process formulation for predicting the

form with prior (learning) as well as its class (prediction). Different datasets are available and

will be used to first learn a model which will build a probability map (with uncertainty) and

then separate the predicted areas of objects.

Skills:

The candidate should be familiar with standard machine learning methods with a solid

background in:

- Programming with python (matlab or R).

- Numerical methods and/or stochastic modeling

Keywords:

Artificial Intelligence, Gaussian processes, Machine learning, Bayesian inference

Description (up to 1 page):

Detecting and classifying multiple forms of objects in a video stream is a key step in different machine

vision systems. The challenge is to propose a method that would be able to distinguish the classes of

objects while detecting them properly. For example, automatic supervision and detection by learning

methods are increasingly used for decision support in industrial, agricultural, food, etc. production

systems. Such a need to automate quality control tasks and to fill the lack of expertise among users raises

very important scientific obstacles. One of the main reasons for this scientific interest is the need to

choose robust models on different criteria capable of providing powerful predictions.

Within the framework of this project, we aim to develop accurate methods that will be able to: (i) extract

relevant patterns (characteristics) from the data, (ii) learn reference shapes, (iii) analyze and compare

any new observation with the reference, (iv) infer on a state, and (v) propose a categorization. All these

steps will be regrouped in a single unified framework based on a stochastic formulation for a

probabilistic decision taking into account uncertainty. Indeed, several recent works have shown that

learning methods, in particular methods based on stochastic optimization and modeling, are the most

suitable when it comes to automatic anomaly detection or variation/growth/changes characterization.

Different datasets will be used to learn and test each model.

References (up to ½ page):

- C. Samir, J-M. Loubes, A.-F. Yao, F.Bachoc. Learning a Gaussian Process Model on the Riemannian

Manifold of Non-decreasing Distribution Functions, PR-ICAI 2019.

- C. Samir, I. Adouani, Regression on Riemannian manifolds, Applied Mathematics and Computation

2019.

- O. M. Essid, C. Samir, H. Laga. Automatic Detection and Classification of Manufacturing Defects in

Metal Boxes Using Deep Learning. PLoS ONE , 2018.

- http://gaussianprocess.org/gpml/

- Giovanni Maria Maggioni and Marco Mazzotti. Stochasticity in Primary Nucleation:

Measuring and Modeling Detection Times. Crystal Growth & Design 2017.

How to candidate?

Applications from abroad are welcome. For questions please feel free to contact Chafik Samir (E-Mail:

chafik.samir@uca.fr). Applications including a CV, a recommendation letter, a list of publications if

any, and descriptions of research and teaching experiences will be appreciated. The application is

requested in electronic form as a single compressed file.

 
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Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Other: Master Degree or equivalent

Skills/Qualifications

 

 
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Work location(s)
1 position(s) available at
Laboratory of Informatics, Modeling and Optimization of Systems (LIMOS)
France
Région Auvergne Rhône-Alpes
AUBIERE
63178
Campus Universitaire des Cézeaux TSA 60125 - CS 60026 1, Rue de la Chebarde

Open, Transparent, Merit based Recruitment procedures of Researchers (OTM-R)

Know more about it at Université Clermont Auvergne

Know more about OTM-R

EURAXESS offer ID: 528439

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