Sistema endoscópico estereoscópico para medição geométrica de uniões soldadas de dutos

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Metrologia Científica e Industrial.Risers rígidos (Steel Catenary Risers - SCR) têm sido considerados fortes candidatos para explotação de petróleo em águas profundas. Sendo assim, existe um grande esforço em garantir a integridade estrutural durante sua vida útil. Atividades de inspeção tornam-se extremamente importantes durante o processo de fabricação. A soldagem entre seções de riser é um processo considerado crítico, tornando-se foco de inspeção. Estudos e análises demonstram que a performance em fadiga da solda é uma questão crítica, principalmente no ponto de contato com o leito oceânico. Neste contexto, controlar os desalinhamentos entre as seções soldadas e a qualidade geométrica da solda pode melhorar a vida em fadiga

Multimodal Three Dimensional Scene Reconstruction, The Gaussian Fields Framework

The focus of this research is on building 3D representations of real world scenes and objects using different imaging sensors. Primarily range acquisition devices (such as laser scanners and stereo systems) that allow the recovery of 3D geometry, and multi-spectral image sequences including visual and thermal IR images that provide additional scene characteristics. The crucial technical challenge that we addressed is the automatic point-sets registration task. In this context our main contribution is the development of an optimization-based method at the core of which lies a unified criterion that solves simultaneously for the dense point correspondence and transformation recovery problems. The new criterion has a straightforward expression in terms of the datasets and the alignment parameters and was used primarily for 3D rigid registration of point-sets. However it proved also useful for feature-based multimodal image alignment. We derived our method from simple Boolean matching principles by approximation and relaxation. One of the main advantages of the proposed approach, as compared to the widely used class of Iterative Closest Point (ICP) algorithms, is convexity in the neighborhood of the registration parameters and continuous differentiability, allowing for the use of standard gradient-based optimization techniques. Physically the criterion is interpreted in terms of a Gaussian Force Field exerted by one point-set on the other. Such formulation proved useful for controlling and increasing the region of convergence, and hence allowing for more autonomy in correspondence tasks. Furthermore, the criterion can be computed with linear complexity using recently developed Fast Gauss Transform numerical techniques. In addition, we also introduced a new local feature descriptor that was derived from visual saliency principles and which enhanced significantly the performance of the registration algorithm. The resulting technique was subjected to a thorough experimental analysis that highlighted its strength and showed its limitations. Our current applications are in the field of 3D modeling for inspection, surveillance, and biometrics. However, since this matching framework can be applied to any type of data, that can be represented as N-dimensional point-sets, the scope of the method is shown to reach many more pattern analysis applications

Epipolar Geometry Estimation by Tensor Voting in 8D

We present a novel, efficient, initialization free approach to the problem of epipolar geometry estimation, by formulating it as one of hyperplane inference from a sparse and noisy point set in an 8D space. Given a set of noisy point correspondences in two imagesas obtained from two views of a static scene without correspondences, even in the presence of moving objects, our method pulls out inlier matches while rejecting outliers. Unlike most methods which optimize certain objective function, our approach does not involve initialization or any search in the parameter space, and therefore is free of the problem of local optima or poor convergence. Since no search is involved, it is unnecessary to impose simplifying assumption (such as affine camera or local planar homography) to the scene being analyzed for reducing the search complexity. Subject to the general epipolar constraint only, we detect wrong matches by establishing salient "extremalities" via a novel approach, 8D Tensor Votin..