Tensor learning for color and polarimetric imaging

30/05/2023 – 31/05/2023 all-day

Offre en lien avec l’Action/le Réseau : – — –/Innovation

Laboratoire/Entreprise : The candidate will be either located at CRAN, Nanc
Durée : up to 6 months
Contact : zniyed@univ-tln.fr
Date limite de publication : 2023-05-30

Contexte :
Many imaging applications rely on the acquisition, processing and analysis of 3D or 4D vectorial data pixels: this includes notably color imaging (red, blue and green channels) or polarimetric imaging (4D Stokes parameters at each pixel). Such multichannel data is often represented using quaternions – a generalization of complex numbers in four dimensions – in order to simplify expressions and leverage unique geometric and physical insights offered by this algebraic representation. Therefore, datasets of color or polarimetric images can be viewed as a collection of quaternion-valued matrices, which form multidimensional quaternion arrays – also called quaternion tensors.

Sujet :
The aim of this internship is to demonstrate the potential of quaternion tensor decompositions for learning features from databases of color and polarimetric images. Quaternion tensor decompositions have only been introduced recently [1]. They generalize usual tensor decompositions
[2] to the quaternion field. The candidate will take advantage of the algorithms proposed in [1]. He / she will focus on two main cases of uses of quaternion tensor decompositions (Canonical Polyadic and Tucker) to

1. learn features from a standard color image database (such as ImageNET)
2. perform source separation on polarimetric hyperspectral data

One key complementary objective will be to benchmark performances of quaternion tensor decompositions
against standard real-domain tensor decompositions.

Profil du candidat :
The candidate should have good writing and oral communication skills.

Formation et compétences requises :
He/she should be enrolled in a M1/M2R or engineer diploma in one or more of the following fields: signal and image processing, machine learning, applied mathematics.

Adresse d’emploi :
Depending on his/her preferences, the candidate will be either located at CRAN, Nancy or either at LIS, Seatech, Toulon.

Document attaché : 202302081818_projet.pdf