Groupwise non-rigid image registration is a powerful tool to automatically establish correspondences across sets of images. Such correspondences are widely used for constructing statistical models of shape and appearance. As existing techniques usually treat registration as an optimisation problem, a good initialisation is required. Although the standard initialisation---affine transformation---generally works well, it is often inadequate when registering images of complex structures. In this thesis we present a sophisticated system that uses the sparse matches of one or more parts+geometry models as the initialisation. We show that both the model/s and its/their matches can be automatically obtained, and that the matches are able to effectively initialise a groupwise non-rigid registration algorithm, leading to accurate dense correspondences. We also show that the dense mesh models constructed during the groupwise registration process can be used to accurately annotate new images. We demonstrate the efficacy of the proposed system on three datasets of increasing difficulty, and report on a detailed quantitative evaluation of its performance.

Progressive mesh (PM) representation can perfectly meet the requirements of generating multi-resolutions for a detailed 3D model. This research proposes a new PM representation method to improve the PM representation storage efficiency and reduce PM generation time. In existing PM representation methods, more than 4 adjacent vertices will be stored for one vertex in the PM representation. Furthermore, the methods always use the inefficient vertex and face list representation during the generation process. In our proposed method, only three vertices are stored by using the √3 subdivision scheme and the efficient half-edge data structure replaces the vertex and face list representation. To evaluate the proposed method, a designed experiment is conducted by using three common testing 3D models. The result illustrates the improvements by comparing to other previous methods. / 0707671386

Orientador: Reginaldo Palazzo Júnior / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-18T16:43:26Z (GMT). No. of bitstreams: 1 Pilla_ElianeCristinaGeroli_M.pdf: 2007393 bytes, checksum: 2b95255e6d4fca123c23a039d1a083a5 (MD5) Previous issue date: 2005 / Resumo: O presente trabalho tem como meta principal construir constelações de sinais geometricamente uniformes no plano hiperbólico, visando considerá-las como alfabeto para geração de códigos de espaço de sinais, em particular os códigos de classes laterais generalizados. Para estabelecer estas constelações foi escolhido um conjunto de sinais geometricamente uniforme, constituído pelos centros dos octógonos da tesselação {8, 8}. Depois foi obtido um rotulamento para os elementos do grupo gerador dos conjuntos de sinais geometricamente uniformes em cada classe lateral. Finalmente, a partir do isomorfismo rótulo obtivemos um rotulamento isométrico para os elementos do conjunto de sinais / Abstract: Our goal in this work is to construct hyperbolic geometrically uniform signal constellations (more specifically g-torus) that are able to act as alphabets for ge neration of codes. To obtain these constellations we choose geometrically uniform signal sets consisting of the centers of the p-gons of tessellations of type {p, q}. From these constellations we obtain labelings for the elements of the generator group of the geometrically uniform signal sets in each coset. Finally, by the label isomorphism we obtain an isometric labeling for the elements of the signal set / Mestrado / Telecomunicações e Telemática / Mestre em Engenharia Elétrica