Jean‐Philippe Vandeborre

IMT Nord Europe

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Mohamed Daoudi

IMT Nord Europe

Hazem Wannous

Université de Lille

Tarik Filali Ansary

Institut Mines-Télécom

Victor Delvigne

University of Mons

Olivier Colot

University of Mons

Guillaume Lavoué

Institut National des Sciences Appliquées de Lyon

Julien Tierny

Sorbonne Université

Christophe Chaillou

Université de Lille

Halim Benhabiles

Université de Lille

Hedi Tabia

Informatique, Biologie Intégrative et Systèmes Complexes

Cooperative Institutions

Centre National de la Recherche Scientifique

Université de Lille

Laboratoire d'Informatique Fondamentale de Lille

Institut national de recherche en informatique et en automatique

Centre de Recherche en Informatique

École Centrale de Lille

IMT Nord Europe

Institut Mines-Télécom

University of Mons

Centre de recherche Inria Lille - Nord Europe

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A Bayesian approach for 3D models retrieval based on characteristic views

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. (2004)

Tarik Filali Ansary Jean‐Philippe Vandeborre Mohamed Daoudi

The management of big databases of three-dimensional models (used in CAD applications, visualization, games, etc.) is a very important domain. The ability to characterize and easily retrieve models are a key issues for the designers and the final users. In this frame, two main approaches exist: search by example of a three-dimensional model, and search by a 2D view. In this paper, we focus on the characterization of a 3D model by a set of views (called characteristic views), and on the indexing process of these models with a Bayesian probabilistic approach using the characteristic views. We illustrate our results using a collection of three-dimensional models supplied by Renault group.

10.1109/icpr.2004.1334673

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Citations (12)

Topology driven 3D mesh hierarchical segmentation

Julien Tierny Jean‐Philippe Vandeborre Mohamed Daoudi

In this paper, we propose to address the semantic- oriented 3D mesh hierarchical segmentation problem, using enhanced topological skeletons. This high level information drives both the feature boundary computation as well as the feature hierarchy definition. Proposed hierarchical scheme is based on the key idea that the topology of a feature is a more important decomposition criterion than its geometry. First, the enhanced topological skeleton of the input triangulated surface is constructed. Then it is used to delimit the core of the object and to identify junction areas. This second step results in a fine segmentation of the object. Finally, a fine to coarse strategy enables a semantic- oriented hierarchical composition of features, subdividing human limbs into arms and hands for example. Method performance is evaluated according to seven criteria enumerated in latest segmentation surveys [3]. Thanks to the high level description it uses as an input, presented approach results, with low computation times, in robust and meaningful compatible hierarchical decompositions.

Feature (linguistics)

Hierarchical database model

10.1109/smi.2007.38

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Citations (52)

3D-model view characterization using equilibrium planes

HAL (Le Centre pour la Communication Scientifique Directe) (2008)

Adrien Theetten Tarik Filali Ansary Jean‐Philippe Vandeborre

We propose a new method for 3D-mesh model characteristic view selection. It consists in using the views that come from the equilibrium states of a 3D-model: they correspond to the horizontal plane on which an object is stat- ically laying under the effect of gravity. The selected views are then very intuitive for the user. Indeed, to present a query, the user will take a photo or draw a sketch of the object on a table or on a floor, putting thus the object in a static mechanical equilibrium. Consequently, our view selection method follows the same principles: finding all the equilibrium planes of an object and obtaining their relative 2D views. We present the experiments and results of our method on the Princeton 3D Shape Benchmark Database using a collection of 50 images (photos, sketches, etc.) as queries, showing the performance of our method in 3D retrieval from photos.

Sketch

Benchmark (surveying)

Table (database)

3d model

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Eye-Gaze Estimation using a Deep Capsule-based Regression Network

Vivien Bernard Hazem Wannous Jean‐Philippe Vandeborre

Eye-gaze information is used in a variety of user platforms, such as driver monitoring systems and head-mounted interfaces. In order to estimate human eye-gaze, many solutions have been proposed, using different devices and techniques. However, achieving such estimation using only cheap devices like RGB cameras would enable gaze interactions on mobile devices and therefore generalise this kind of interaction. It could also enable behavior studies based on gaze and made on every day devices. We propose in this paper a new method for eye-gaze estimation using a new deep learning architecture based on the Capsule Neural Network. Capsule Networks have shown great results so far on classification tasks, but only a few works use them for regression tasks.By taking advantage of the Capsule Network architecture and its ability to reconstruct images, we are able to recreate simplified eye images and then estimate human gaze from them. Experiments are performed on two representative datasets for the task of eye-gaze estimation. Encouraging results are obtained for both the estimation and the reconstruction.

RGB color model

10.1109/cbmi50038.2021.9461895

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Citations (7)

SHREC'12 Track: 3D mesh segmentation

HAL (Le Centre pour la Communication Scientifique Directe) (2012)

Guillaume Lavoué Jean‐Philippe Vandeborre Halim Benhabiles Mohamed Daoudi Kai Huebner

3D mesh segmentation is a fundamental process in many applications such as shape retrieval, compression, deformation, etc. The objective of this track is to evaluate the performance of recent segmentation methods using a ground-truth corpus and an accurate similarity metric. The ground-truth corpus is composed of 28 watertight models, grouped in five classes (animal, furniture, hand, human and bust) and each associated with 4 ground-truth segmentations done by human subjects. 3 research groups have participated to this track, the accuracy of their segmentation algorithms have been evaluated and compared with 4 other state-of-the-art methods.

Ground truth

Similarity (geometry)

10.2312/3dor/3dor12/093-099

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PhyDAA: Physiological Dataset Assessing Attention

IEEE Transactions on Circuits and Systems for Video Technology (2021)

Victor Delvigne Hazem Wannous Thierry Dutoit Laurence Ris Jean‐Philippe Vandeborre

Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent neurodevelopmental disorder among children. It affects patients' lives in many ways: inattention, difficulty with stimuli inhibition or motor function regulation. Different treatments exist today, but these can present side effects or are not effective for all subgroups. Neurofeedback (NF) is an innovative treatment consisting of brain activity display. NF training could consist of a virtual reality (VR) video-game in which the participant's attention affects the game. Attention being assessed through physiological signals, one of the main steps is to design an estimator for the attention state. We present a novel framework able to record physiological signals in specific attention states and able to estimate the corresponding attention state. We propose a database composed of electroencephalography signals (EEG), and an eye-tracker labelled with a score representing the attention span for 32 healthy participants. Different features are extracted from the signals and machine learning (ML) algorithms are proposed. Our approach exhibits high accuracy for attention estimation, which corroborates a correlation between attention state and physiological signals (i.e. EEG, eye-tracking signals). The dataset has been made publicly available to promote research in the domain and we encourage other scientists to use their own approach for attention estimation.

Attention span

10.1109/tcsvt.2021.3061719

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Citations (30)

Chapitre 18 - Reconnaissance et indexation 3D

HAL (Le Centre pour la Communication Scientifique Directe) (2013)

Jean‐Philippe Vandeborre Hedi Tabia Mohamed Daoudi

With the increasing usage of 3D models, the need for efficient shape retrieval techniques became mandatory. In this chapter, several approaches for 3D retrieval and shape recognition are presented. Invariant descriptor issues for 3D static mesh are exposed, before addressing action and face recognition in 3D videos. Then classical 3D-search engine performance evaluation tools are thoroughly explained. Finally, examples of significant applications using 3D retrieval techniques are given.

Indexation

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Géodésiques sémantiques pour la description et la labélisation automatique de parties

HAL (Le Centre pour la Communication Scientifique Directe) (2014)

Vincent Léon Nicolas Bonneel Guillaume Lavoué Jean‐Philippe Vandeborre

Les applications qui reposent sur une base de modeles 3D n'utilisent generalement pas les donnees 3D brutes mais plutot des meta-donnees obtenues manuellement ou automatiquement et qui resument le contenu de la base. Ces meta-donnees peuvent etre des descripteurs purement geometriques, mais qui peuvent aussi porter un sens semantique. Le plus souvent, l'information semantique est basee sur une decomposition du modele, obtenue par segmentation, ou un label est assigne a chaque segment de maniere automatique ou manuelle. Nous proposons ici un descripteur a la fois geometrique et semantique qui n'est pas uniforme sur un segment, mais qui definit la semantique a la surface du modele comme un ensemble de distances geodesiques avec des points d'ancrage de semantique connue. Ce descripteur encode ainsi naturellement les relations topologiques entre les differents labels. Nous proposons ensuite une application de ce descripteur a la labelisation automatique de segments.

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A parts-based approach for automatic 3D shape categorization using belief functions

ACM Transactions on Intelligent Systems and Technology (2013)

Hedi Tabia Mohamed Daoudi Jean‐Philippe Vandeborre Olivier Colot

Grouping 3D objects into (semantically) meaningful categories is a challenging and important problem in 3D mining and shape processing. Here, we present a novel approach to categorize 3D objects. The method described in this article, is a belief-function-based approach and consists of two stages: the training stage, where 3D objects in the same category are processed and a set of representative parts is constructed, and the labeling stage, where unknown objects are categorized. The experimental results obtained on the Tosca-Sumner and the Shrec07 datasets show that the system efficiently performs in categorizing 3D models.

10.1145/2438653.2438668

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Citations (13)

Where Is My Mind (looking at)? Predicting Visual Attention from Brain Activity

arXiv (Cornell University) (2022)

Victor Delvigne Noé Tits Luca La Fisca Nathan Hubens Antoine Maiorca

Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, artificial intelligence and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, we show that visual attention can be retrieved from EEG acquisition. The results are comparable to traditional predictions from observed images, which is of great interest. For this purpose, a set of signals has been recorded and different models have been developed to study the relationship between visual attention and brain activity. The results are encouraging and comparable with other approaches estimating attention with other modalities. The codes and dataset considered in this paper have been made available at \url{https://figshare.com/s/3e353bd1c621962888ad} to promote research in the field.

Modalities

10.48550/arxiv.2201.03902

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