SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation

A novel approach of data augmentation based on irregular superpixel decomposition is proposed. This approach called SuperpixelGridMasks permits to extend original image datasets that are required by training stages of machine learning-related analysis architectures towards increasing their performances. Three variants named SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix are presented. These grid-based methods produce a new style of image transformations using the dropping and fusing of information. Extensive experiments using various image classification models and datasets show that baseline performances can be significantly outperformed using our methods. The comparative study also shows that our methods can overpass the performances of other data augmentations. Experimental results obtained over image recognition datasets of varied natures show the efficiency of these new methods. SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix codes are publicly available at https://github.com/hammoudiproject/SuperpixelGridMasksComment: The project is available at https://github.com/hammoudiproject/SuperpixelGridMask

Généralisation du diagramme de Voronoï et placement de formes géométriques complexes dans un nuage de points.

La géométrie algorithmique est une discipline en pleine expansion dont l'objet est la conception d'algorithmes résolvant des problèmes géométriques. De tels algorithmes sont très utiles notamment dans l'ingénierie, l'industrie et le multimédia. Pour être performant, il est fréquent qu'un algorithme géométrique utilise des structures de données spécialisées.Nous nous sommes intéressés à une telle structure : le diagramme de Voronoï et avons proposé une généralisation de celui-ci. Ladite généralisation résulte d'une extension du prédicat du disque vide (prédicat propre à toute région de Voronoï) à une union de disques. Nous avons analysé les régions basées sur le prédicat étendu et avons proposé des méthodes pour les calculer par ordinateur.Par ailleurs, nous nous sommes intéressés aux problèmes de placement de formes , thème récurrent en géométrie algorithmique. Nous avons introduit un formalisme universel pour de tels problèmes et avons, pour la première fois, proposé une méthode de résolution générique, en ce sens qu'elle est apte à résoudre divers problèmes de placement suivant un même algorithme.Nos travaux présentent, d'une part, l'avantage d'élargir le champ d'application de structures de données basées sur Voronoï. D'autre part, ils facilitent de manière générale l'utilisation de la géométrie algorithmique, en unifiant définitions et algorithmes associés aux problèmes de placement de formes.Computational geometry is an active branch of computer science whose goal is the design of efficient algorithms solving geometric problems. Such algorithms are useful in domains like engineering, industry and multimedia. In order to be efficient, algorithms often use special data structures.In this thesis we focused on such a structure: the Voronoi diagram. We proposed a new generalized diagram. We have proceeded by extending the empty disk predicate (satisfied by every Voronoi region) to an arbitrary union of disks. We have analyzed the new plane regions based on the extended predicate, and we designed algorithms for computing them.Then, we have considered another topic, which is related to the first one: shape placement problems. Such problems have been studied repeatedly by researchers in computational geometry. We introduced new notations along with a global framework for such problems. We proposed, for the first time a generic method, which is able to solve various placement problems using a single algorithm.Thus, our work extend the scope of Voronoi based data structures. It also simplifies the practical usage of placement techniques by unifying the associated definitions and algorithms.MULHOUSE-SCD Sciences (682242102) / SudocSudocFranceF

Analyse et gestion de l’occupation de places de stationnement par vision artificielle

Cet article présente un système de surveillance basé sur la vision pour le développement de services de gestion de places de parking. Le système présenté est un système adaptable pour l'analyse de places de stationnement dans des parkings de différentes configurations. Dans ce but, des expérimentations ont été menées sous différentes prises de vue en utilisant une caméra connectée à une station de travail mobile. Les résultats obtenus montrent la faisabilité du système dans l'analyse et dans la gestion des emplacements de parking avec des véhicules

SHREC2020 track:Multi-domain protein shape retrieval challenge

Proteins are natural modular objects usually composed of several domains, each domain bearing a specific function that is mediated through its surface, which is accessible to vicinal molecules. This draws attention to an understudied characteristic of protein structures: surface, that is mostly unexploited by protein structure comparison methods. In the present work, we evaluated the performance of six shape comparison methods, among which three are based on machine learning, to distinguish between 588 multi-domain proteins and to recreate the evolutionary relationships at the proteinand species levels of the SCOPe database. The six groups that participated in the challenge submitted a total of 15 sets of results. We observed that the performance of all the methods significantly decreases at the species level, suggesting that shape-only protein comparison is challenging for closely related proteins. Even if the dataset is limited in size (only 588 proteins are considered whereas more than 160,000 protein structures are experimentally solved), we think that this work provides useful insights into the current shape comparison methods performance, and highlights possible limitations to large-scale applications due to the computational cost

Surface-based protein domains retrieval methods from a SHREC2021 challenge

publication dans une revue suite à la communication hal-03467479 (SHREC 2021: surface-based protein domains retrieval)International audienceProteins are essential to nearly all cellular mechanism and the effectors of the cells activities. As such, they often interact through their surface with other proteins or other cellular ligands such as ions or organic molecules. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence similar 3D surface properties (shape, physico-chemical properties, …). The protein surfaces are therefore of primary importance for their activity. In the present work, we assess the ability of different methods to detect such similarities based on the geometry of the protein surfaces (described as 3D meshes), using either their shape only, or their shape and the electrostatic potential (a biologically relevant property of proteins surface). Five different groups participated in this contest using the shape-only dataset, and one group extended its pre-existing method to handle the electrostatic potential. Our comparative study reveals both the ability of the methods to detect related proteins and their difficulties to distinguish between highly related proteins. Our study allows also to analyze the putative influence of electrostatic information in addition to the one of protein shapes alone. Finally, the discussion permits to expose the results with respect to ones obtained in the previous contests for the extended method. The source codes of each presented method have been made available online

Molecular graphs : a generalization of the Delaunay triangulation.

International audienc

Approches geometriques par modeles de Voronoi en segmentation d'images

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : T 82699 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Molecular graphs : a generalization of the Delaunay triangulation.

International audienc

Three-dimensional shapes of a finite set of points

International audienceThe three-dimensional A-shape is based on a mathematical formalism which determines exact relationships between points and shapes. It reconstructs surface and volume and detects 3D dot patterns for a given point cloud. A-shape of a set of points is a sub-complex of Delaunay triangulation of this set. It generates a family of shapes according to the selected A (a set of points). A method to compute the positions of the points of A is proposed. These points are selected from the vertices of Voronoi diagram by analyzing the form of the polytopes; their elongation. This method allows the A-shape to reflect different levels of detail in different parts of space. An efficient algorithm computing the three-dimensional A-shape is presented, the A-shape of a set of points is derived from the Delaunay triangulation of the same set. The speed of the algorithm is determined by the speed of the algorithm computing the Delaunay triangulation

Approches géométriques par modèles de Voronoi͏̈ en segmentation d'images

Cette thèse décrit l'exploitation d'approches géométriques pour résoudre le probleme de segmentation d'images, ainsi que le probleme de détection d'événements en imagerie multi-sources. La structure géométrique utilisée est le diagramme de Voronoi ponctuel. Nous abordons tout d'abord une première approche de segmentation d'images en polygones de Voronoi dont l'algorithme fonctionne en trois étapes: une phase d'initialisation, une phrase de partition et une phase de fusion. Ce processus de segmentation est utilise pour détecter les différences entre des images acquises dans différents domaines de radiometrie. Nous présentons une seconde technique de segmentation fondée sur le processus de coopération d'un détecteur de frontières et d'un détecteur de régions. Cette approche utilise le diagramme de voronoi généralisé pour générer une partition initiale dont les frontières des régions s'appuient sur les contours détectés initialement. Pour réaliser cette segmentation nous avons propose un algorithme de calcul d'une approximation du diagramme de Voronoi généralisé en utilisant le diagramme de Voronoi ponctue