Skeletal shape representations, in spite of their structural instabilities, have proven themselves as effective representation schemes for recognition and classification of visual shapes. They capture part structure in a compact and natural way and provide insensitivity to visual transformations such as occlusion and articulation of parts. In this thesis, we explore the potential use of disconnected skeleton representation for shape recognition and shape classification. Specifically, we first investigate the importance of contextual information in recognition where we extend the previously proposed disconnected skeleton based shape matching methods in different ways by incorporating category knowledge into matching process. Unlike the view in syntactic matching of shapes, our interpretation differentiates the semantic roles of the shapes in comparison in a way that a query shape is being matched with a database shape whose category is known a priori. The presence of context, i.e. the knowledge about the category of the database shape, influences the similarity computations, and helps us to obtain better matching performance. Next, we build upon our category-influenced matching framework in which both shapes and shape categories are represented with depth-1 skeletal trees, and develop a similarity-based shape classification method where the category trees formed for each shape category provide a reference set for learning the relationships between categories. As our classification method takes into account both within-category and between-category information, we attain high classification performance. Moreover, using the suggested classification scheme in a retrieval task improves both the efficiency and accuracy of matching by eliminating unrelated comparisons.

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Cette thèse porte sur la reconnaissance et l’appariement de formes 3D pour des systèmes intelligents de vision par ordinateur. Elle décrit deux contributions principales à ce domaine. La première contribution est une implémentation d'un nouveau descripteur de formes construit à la base de la géométrie spectrale de l'opérateur de Laplace-Beltrami ; nous proposons une signature de point globale avancée (AGPS). Ce descripteur exploite la structure intrinsèque de l'objet et organise ses informations de manière efficace. De plus, AGPS est extrêmement compact puisque seulement quelques paires propres étaient nécessaires pour obtenir une description de forme précise. La seconde contribution est une amélioration de la signature du noyau d'onde ; nous proposons une signature du noyau d'onde optimisée (OWKS). La perfectionnement est avec un algorithme heuristique d'optimisation par essaim de particules modifié pour mieux rapprocher une requête aux autres formes appartenant à la même classe dans la base de données. L'approche proposée améliore de manière significative la capacité discriminante de la signature. Pour évaluer la performance de l'approche proposée pour la récupération de forme 3D non rigide, nous comparons le descripteur global d'une requête aux descripteurs globaux du reste des formes de l'ensemble de données en utilisant une mesure de dissimilarité et trouvons la forme la plus proche. Les résultats expérimentaux sur différentes bases de données de formes 3D standards démontrent l'efficacité des approches d'appariement et de récupération proposées par rapport aux autres méthodes de l'état de l'art. / This thesis concerns recognition and matching of 3D shapes for intelligent computer vision systems. It describes two main contributions to this domain. The first contribution is an implementation of a new shape descriptor built on the basis of the spectral geometry of the Laplace-Beltrami operator; we propose an Advanced Global Point Signature (AGPS). This descriptor exploits the intrinsic structure of the object and organizes its information in an efficient way. In addition, AGPS is extremely compact since only a few eigenpairs were necessary to obtain an accurate shape description. The second contribution is an improvement of the wave kernel signature; we propose an optimized wave kernel signature (OWKS). The refinement is with a modified particle swarm optimization heuristic algorithm to better match a query to other shapes belonging to the same class in the database. The proposed approach significantly improves the discriminant capacity of the signature. To assess the performance of the proposed approach for nonrigid 3D shape retrieval, we compare the global descriptor of a query to the global descriptors of the rest of shapes in the dataset using a dissimilarity measure and find the closest shape. Experimental results on different standard 3D shape benchmarks demonstrate the effectiveness of the proposed matching and retrieval approaches in comparison with other state-of-the-art methods.

This thesis investigated how shapes can be mapped to programming constructs, offering a new way to compose and understand code with the long term goal of creating a tactile programming tool. By doing so it delved into the challenges of translating shapes into abstract programming concepts. Existing programming tools rely heavily on visual interfaces, making them inaccessible to individuals with visual impairments. Similar endeavours to create tactile programming tools were analysed and were shown to be domain-specific rather than Turing-complete which greatly limits their usefulness. The solution was to map a set of shapes to a set of Brainfuck (BF) instructions and classifying these shapes with a Support Vector Machine (SVM). Results are promising but are as of yet untested in less than ideal conditions, such as it would be in a real world application. More work has to be done to reach the goal of a tactile programming tool accessible to individuals with visual impairments. / Denna avhandling undersökte hur former kan kartläggas till programmerings-konstruktioner, vilket erbjuder ett nytt sätt att komponera och förstå kod med ett långsiktigt mål att skapa ett taktilt programmingsverktyg. Genom att göra det går den in på utmaningarna med att översätta former till abstrakta programmeringskoncept. Befintliga programmeringsverktyg förlitar sig i hög grad på visuella gränssnitt, vilket gör dem otillgängliga för personer med synnedsättningar. Liknande försök att skapa taktila programmeringsverktyg analyserades och visades vara domänspecifika snarare än Turing-kompletta, vilket starkt begränsar deras användbarhet. Lösningen var att kartlägga en uppsättning former till en uppsättning Brainfuck (BF)-instruktioner och klassificera dessa former med en Support Vector Machine (SVM). Resultaten är lovande men har ännu inte testats under mindre än ideala förhållanden, såsom det skulle vara i en verklig tillämpning. Mer arbete måste göras för att nå målet med ett taktilt programmeringsverktyg som är tillgängligt för personer med synnedsättningar.