BMVC 2013, 3DOR2014

I'll be at BMVC in Bristol next week to present some work done with Hedi Tabia on 3D similarities using curves.

Similarity on curves is usually very expansive since it requires to re-parametrize one of the curves so as to map it to the same affine space as the other. To circumvent the heavy processing, we proposed to use the Nyström approximation for kernels on some well chosen training set found by active learning. Nothing excessively fancy, but very very effective in computational time.

On the other hand, Hedi is co-organizing the next 3DOR 2014, which will be held in Strasbourg just before Eurographics. The website is brand new, so expect more information soon.

Some publications in 2013

The year is beginning with a small batch of publications on the different topics I'm working on.

Two years ago, we developed a new signature for image retrieval and classification based on tensors aggregation we named VLAT. The paper giving the very details of the method (plus a bonus for cheap large scale computation) has now been published in Computer Vision and Image Understanding at Elsevier. In the meantime, Romain Negrel (Ph.D. Student) has completely redesigned the method to improve its effectiveness. His work has now been accepted in IEEE Multimedia. There are some nice experiments in this paper, including large scale retrieval (1M images) at very low bitrate (less than 64 bytes per image).

On the video front, we have a paper accepted at MVA 2013 with Olivier Kihl (PostDoc), on video descriptors using polynomials expansion. We have very good results on well known data-sets, which makes me think this approach sounds very promising.

In 3D object retreival, we have an accepted paper at 3DOR with Hedi Tabia. This was a pretty straight forward extension of our still images indexing methods to 3D Objects, and it works well.

On a totally different topic, I recently did a paper with my colleague Aymeric Histace on the modeling of an insect (the bark beetle) using a multi-agents system. This was something I haven't done for years, and it was fun to do. The novelty in our approach is that we consider the chemical markers released by the agents and the environment to evolved thanks to a partial differential equations system modeling the physical spreading. This concurrent evolution between MAS and PDE makes the behavior of the agents a lot less predictable. This work was in part done by Marie-Charlotte Desseroit (undergrad student) during an internship last summer, which I find pretty impressive.

Seminar by Hassen Drira, Thursday 8th march 2012

Tomorrow, Hassen will be showing us some pretty things on 3D object recognition (in french). It's open to everyone, room 384 at the ENSEA.

Calcul statistique sur les variétés de formes 3D pour la reconnaissance d'identité et d'expressions

Nous proposons un cadre Riemannien pour comparer, déformer, calculer des statistiques et organiser de manière hiérarchique des surfaces faciales. Nous appliquons ce cadre à la biométrie faciale 3D indépendamment des expressions faciales. Le même framework est utilisé pour reconnaitre les expressions indépendamment de l'identité. Les surfaces faciales sont représentées par un ensemble de courbes radiales. Dans ce cas, le calcul se simplifie et l'espace des formes des courbes ouvertes se ramène à une hyper sphère de l'espace de Hilbert. Le reconnaissance d'identité est basée sur une métrique élastique afin de faire face aux déformations non-isomètriques (ne conservant pas les longueurs) des surfaces faciales. La reconnaissance d'expressions, quand à elle, est basée sur l'apprentissage de l'énergie nécessaire à déformer les visages neutres pour exprimer les six émotions universelles. L'approche de reconnaissance d'identité proposée a été validée sur des Benchmarks connus (FRGCv2, GAVAB, BOSPHORUS) et a obtenu des résultats compétitifs par rapport aux méthodes de l'état de l'art. L'approche de reconnaissance d'expressions a été testée sur la base BU4D, une base de séquences 3D, et surpasse en performance les approches de l'état de l'art.