Reconstrução e caracterização de estruturas anatómicas exteriores usando visão activa

Este trabalho teve como principais objectivos a familiarização e análise de técnicas de Visão Computacional para a Reconstrução Tridimensional de Objectos, tendo sido iniciado o desenvolvimento de uma plataforma computacional.O presente relatório é constituído por três capítulos: no primeiro, são definidos os objectivos; no segundo, é apresentada a plataforma computacional, assim como alguns resultados experimentais; no terceiro e último capítulo, são apresentadas as conclusões relativas ao estudo e ao desenvolvimento realizado e, finalmente, são referidas as perspectivas de trabalho futuro.The work carried through had as main goals the familiarization and analysis of Computer Vision techniques for Three-Dimensional Reconstruction of Objects. From this work the development of a computer platform has been initiated.The present report has three chapters: in the first one, the goals are defined; in the second, the computer platform is presented, as well as some experimental results; in the third and last chapter, the conclusions relative to the study and work done are drawn and, finally, some perspectives of future work are given

Desenvolvimento de uma Plataforma Computacional para Obtenção da Forma 3D de Objectos usando Técnicas de Visão Activa

Neste artigo pretende-se descrever uma plataforma computacional que está a ser desenvolvida para obter a forma 3D de objectos usando técnicas de Visão Activa. Assim, partindo-se de uma sequência de imagens não calibradas do objecto a reconstruir, usando a referida plataforma, pretende-se obter a geometria 3D do objecto em causa

Structure from Motion with Higher-level Environment Representations

Computer vision is an important area focusing on understanding, extracting and using the information from vision-based sensor. It has many applications such as vision-based 3D reconstruction, simultaneous localization and mapping(SLAM) and data-driven understanding of the real world. Vision is a fundamental sensing modality in many different fields of application. While the traditional structure from motion mostly uses sparse point-based feature, this thesis aims to explore the possibility of using higher order feature representation. It starts with a joint work which uses straight line for feature representation and performs bundle adjustment with straight line parameterization. Then, we further try an even higher order representation where we use Bezier spline for parameterization. We start with a simple case where all contours are lying on the plane and uses Bezier splines to parametrize the curves in the background and optimize on both camera position and Bezier splines. For application, we present a complete end-to-end pipeline which produces meaningful dense 3D models from natural data of a 3D object: the target object is placed on a structured but unknown planar background that is modeled with splines. The data is captured using only a hand-held monocular camera. However, this application is limited to a planar scenario and we manage to push the parameterizations into real 3D. Following the potential of this idea, we introduce a more flexible higher-order extension of points that provide a general model for structural edges in the environment, no matter if straight or curved. Our model relies on linked B´ezier curves, the geometric intuition of which proves great benefits during parameter initialization and regularization. We present the first fully automatic pipeline that is able to generate spline-based representations without any human supervision. Besides a full graphical formulation of the problem, we introduce both geometric and photometric cues as well as higher-level concepts such overall curve visibility and viewing angle restrictions to automatically manage the correspondences in the graph. Results prove that curve-based structure from motion with splines is able to outperform state-of-the-art sparse feature-based methods, as well as to model curved edges in the environment

Space Carving with a Hand-Held Camera

This paper presents a 3D scene reconstruction method, based on space carving, that works with a hand-held camera. In our system, the intrinsic and extrinsic parameters of the camera are determined at the moment of image capture, as opposed to other systems that rely on fixed pre-calibrated camera setups. In order to do this we place a special calibration pattern in the scene in such a way that it does not alter scene visibility. However, the calibration pattern may be partially occluded by the objects of interest in the scene. This has led us to adopt a calibration method based on model recognition. Scene reconstruction is obtained from the set of input images by an adaptive space-carving algorithm that uses not only photometric information but also segmentation information. The segmentation information of a given input image is determined by a robust statistical test based on an approximate model of the scene's background. Such model is computed from a set of images of the scene's background that are warped in such a way that they match the geometry of the desired camera