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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Partial 3D-shape indexing and retrieval

El Khoury, Rachid 22 March 2013 (has links) (PDF)
A growing number of 3D graphic applications have an impact on today's society. These applications are being used in several domains ranging from digital entertainment, computer aided design, to medical applications. In this context, a 3D object search engine with a good performance in time consuming and results becomes mandatory. We propose a novel approach for 3D-model retrieval based on closed curves. Then we enhance our method to handle partial 3D-model retrieval. Our method starts by the definition of an invariant mapping function. The important properties of a mapping function are its invariance to rigid and non rigid transformations, the correct description of the 3D-model, its insensitivity to noise, its robustness to topology changes, and its independance on parameters. However, current state-of-the-art methods do not respect all these properties. To respect these properties, we define our mapping function based on the diffusion and the commute-time distances. To prove the properties of this function, we compute the Reeb graph of the 3D-models. To describe the whole 3D-model, using our mapping function, we generate indexed closed curves from a source point detected automatically at the center of a 3D-model. Each curve describes a small region of the 3D-model. These curves lead to create an invariant descriptor to different transformations. To show the robustness of our method on various classes of 3D-models with different poses, we use shapes from SHREC 2012. We also compare our approach to existing methods in the state-of-the-art with a dataset from SHREC 2010. For partial 3D-model retrieval, we enhance the proposed method using the Bag-Of-Features built with all the extracted closed curves, and show the accurate performances using the same dataset
2

Partial 3D-shape indexing and retrieval / Indexation partielle de modèles 3D

El Khoury, Rachid 22 March 2013 (has links)
Un nombre croissant d’applications graphiques 3D ont un impact sur notre société. Ces applications sont utilisées dans plusieurs domaines allant des produits de divertissement numérique, la conception assistée par ordinateur, aux applications médicales. Dans ce contexte, un moteur de recherche d’objets 3D avec de bonnes performances en résultats et en temps d’exécution devient indispensable. Nous proposons une nouvelle méthode pour l’indexation de modèles 3D basée sur des courbes fermées. Nous proposons ensuite une amélioration de notre méthode pour l’indexation partielle de modèles 3D. Notre approche commence par la définition d’une nouvelle fonction d’application invariante. Notre fonction d’application possède des propriétés importantes : elle est invariante aux transformations rigides et non rigides, elle est insensible au bruit, elle est robuste à de petits changements topologiques et elle ne dépend pas de paramètres. Cependant, dans la littérature, une telle fonction qui respecte toutes ces propriétés n’existe pas. Pour respecter ces propriétés, nous définissons notre fonction basée sur la distance de diffusion et la distance de migration pendulaire. Pour prouver les propriétés de notre fonction, nous calculons le graphe de Reeb de modèles 3D. Pour décrire un modèle 3D complet, en utilisant notre fonction d’application, nous définissons des courbes de niveaux fermées à partir d’un point source détecté automatiquement au centre du modèle 3D. Chaque courbe décrit alors une région du modèle 3D. Ces courbes créent un descripteur invariant à différentes transformations. Pour montrer la robustesse de notre méthode sur différentes classes de modèles 3D dans différentes poses, nous utilisons des objets provenant de SHREC 2012. Nous comparons également notre approche aux méthodes de l’état de l’art à l’aide de la base SHREC 2010. Pour l’indexation partielle de modèles 3D, nous améliorons notre approche en utilisant la technique sacs de mots, construits à partir des courbes fermées extraites, et montrons leurs bonnes performances à l’aide de la base précédente / A growing number of 3D graphic applications have an impact on today’s society. These applications are being used in several domains ranging from digital entertainment, computer aided design, to medical applications. In this context, a 3D object search engine with a good performance in time consuming and results becomes mandatory. We propose a novel approach for 3D-model retrieval based on closed curves. Then we enhance our method to handle partial 3D-model retrieval. Our method starts by the definition of an invariant mapping function. The important properties of a mapping function are its invariance to rigid and non rigid transformations, the correct description of the 3D-model, its insensitivity to noise, its robustness to topology changes, and its independance on parameters. However, current state-of-the-art methods do not respect all these properties. To respect these properties, we define our mapping function based on the diffusion and the commute-time distances. To prove the properties of this function, we compute the Reeb graph of the 3D-models. To describe the whole 3D-model, using our mapping function, we generate indexed closed curves from a source point detected automatically at the center of a 3D-model. Each curve describes a small region of the 3D-model. These curves lead to create an invariant descriptor to different transformations. To show the robustness of our method on various classes of 3D-models with different poses, we use shapes from SHREC 2012. We also compare our approach to existing methods in the state-of-the-art with a dataset from SHREC 2010. For partial 3D-model retrieval, we enhance the proposed method using the Bag-Of-Features built with all the extracted closed curves, and show the accurate performances using the same dataset

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