Examinando por Materia "Nube de puntos"
Mostrando 1 - 8 de 8
Resultados por página
Opciones de ordenación
Ítem Computational Geometry in Medical Applications(Universidad EAFIT, 2016) Cortés Acosta, Camilo Andrés; Ruíz Salguero, Óscar Eduardo; Flórez Esnal, JuliánÍtem Computational geometry in the preprocessing of point clouds for surface modeling(1998) Ruíz, O.E.; Posada, J.L.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEIn Computer Aided Geometric Design ( CAGD ) the automated fitting of surfaces to massive series of data points presents several difficulties: (i) even the formal definition of the problem is ambiguous because the mathematical characteristics (continuity, for example) of the surface fit are dependent on non-geometric considerations, (ii) the data has an stochastic sampling component that cannot be taken as literal, and, (iii) digitization characteristics, such as sampling interval and directions are not constant, etc -- In response, this investigation presents a set of computational tools to reduce, organize and re-sample the data set to fit the surface -- The routines have been implemented to be portable across modeling or CAD servers -- A case study is presented from the footwear industry, successfully allowing the preparation of a foreign, neutral laser digitization of a last for fitting a B-spline surface to it -- Such a result was in the past attainable only by using proprietary software, produced by the same maker of the digitizing hardwareÍtem DigitLAB, an Environment and Language for Manipulation of 3D Digitizations(Presses internationales Polytechnique, 2000) Ruíz, Óscar E.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEIn Computer Aided Geometric Design the fitting of surfaces to massive series of data points has many applications, ranging from medicine to aerophotogrametry -- However, even the mathematical meaning of fitting a surface to a set of points is dependent on functional considerations, and not only on the geometric properties of the point set -- Also, characteristics of some parts of the data set must be interpreted as stochastic in nature, while others must be taken as literal and therefore they become constraints of the surface -- For these reasons, among others, automated surface fitting alone does not produce results usable at industrial level -- At the same time, it does not take advantage of sampling patterns, particular shapes of the cross sections, functionally different regions within the object, etc -- The latest literature reviews show the need for utilities to process point data sets that must be asynchronous, (applicable at any time and upon any region of the point set) -- Addressing this need, this article reports new tools developed within DigitLAB, a language that allows topological traversal, retrieval and statistical modifications to the data, and surface fitting -- They can handle arbitrary topology, as case studies in medicine, mathematics, landscaping, etc discussed here demonstrateÍtem Ellipse-based Principal Component Analysis for Self-intersecting Curve Reconstruction from Noisy Point Sets(Springer Berlin Heidelberg, 2011) Ruíz, O.; Vanegas, C.; Cadavid, C.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAESurface reconstruction from cross cuts usually requires curve reconstruction from planar noisy point samples -- The output curves must form a possibly disconnected 1manifold for the surface reconstruction to proceed -- This article describes an implemented algorithm for the reconstruction of planar curves (1manifolds) out of noisy point samples of a sel-fintersecting or nearly sel-fintersecting planar curve C -- C:[a,b]⊂R→R is self-intersecting if C(u)=C(v), u≠v, u,v∈(a,b) (C(u) is the self-intersection point) -- We consider only transversal self-intersections, i.e. those for which the tangents of the intersecting branches at the intersection point do not coincide (C′(u)≠C′(v)) -- In the presence of noise, curves which self-intersect cannot be distinguished from curves which nearly sel fintersect -- Existing algorithms for curve reconstruction out of either noisy point samples or pixel data, do not produce a (possibly disconnected) Piecewise Linear 1manifold approaching the whole point sample -- The algorithm implemented in this work uses Principal Component Analysis (PCA) with elliptic support regions near the selfintersections -- The algorithm was successful in recovering contours out of noisy slice samples of a surface, for the Hand, Pelvis and Skull data sets -- As a test for the correctness of the obtained curves in the slice levels, they were input into an algorithm of surface reconstruction, leading to a reconstructed surface which reproduces the topological and geometrical properties of the original object -- The algorithm robustly reacts not only to statistical noncorrelation at the self-intersections(nonmanifold neighborhoods) but also to occasional high noise at the nonselfintersecting (1manifold) neighborhoodsÍtem Extending Marching Cubes with Adaptative Methods to obtain more accurate iso-surfaces(Springer Berlin Heidelberg, 2010) Congote, John; Moreno, Aitor; Barandiaran, Iñigo; Barandiaran, Javier; Ruíz, Oscar; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEThis work proposes an extension of the Marching Cubes algorithm, where the goal is to represent implicit functions with higher accuracy using the same grid size -- The proposed algorithm displaces the vertices of the cubes iteratively until the stop condition is achieved -- After each iteration, the difference between the implicit and the explicit representations is reduced, and when the algorithm finishes, the implicit surface representation using the modified cubical grid is more accurate, as the results shall confirm -- The proposed algorithm corrects some topological problems that may appear in the discretization process using the original gridÍtem Parametric Curve Reconstruction from Point Clouds using Minimization Techniques(SCITEPRESS, 2013) Ruíz, Óscar E.; Cortés, C.; Aristizábal, M.; Acosta, Diego A.; Vanegas, Carlos A.; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAESmooth (C1-, C2-,...) curve reconstruction from noisy point samples is central to reverse engineering, medical imaging, etc -- Unresolved issues in this problem are (1) high computational expenses, (2) presence of artifacts and outlier curls, (3) erratic behavior at self-intersections and sharp corners -- Some of these issues are related to non-Nyquist (i.e. sparse) samples -- Our work reconstructs curves by minimizing the accumulative distance curve cs. point sample -- We address the open issues above by using (a) Principal Component Analysis (PCA) pre-processing to obtain a topologically correct approximation of the sampled curve -- (b) Numerical, instead of algebraic, calculation of roots in point-to-curve distances -- (c) Penalties for curve excursions by using point cloud to - curve and curve to point cloud -- (d) Objective functions which are economic to minimize -- The implemented algorithms successfully deal with self - intersecting and / or non-Nyquist samples -- Ongoing research includes self-tuning of the algorithms and decimation of the point cloud and the control polygonÍtem Robust CT to US 3D-3D Registration by Using Principal Component Analysis and Kalman Filtering(2016-07) Echeverría, Rebeca; Cortes, Camilo; Bertelsen, Alvaro; Macia, Ivan; Ruíz, Óscar E.; Flórez, Julián; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAEAlgorithms based on the unscented Kalman filter (UKF) have been proposed as an alternative for registration of point clouds obtained from vertebral ultrasound (US) and computerised tomography (CT) scans, effectively handling the US limited depth and low signaltonoise ratio -- Previously proposed methods are accurate, but their convergence rate is considerably reduced with initial misalignments of the datasets greater than or 30 mm -- We propose a novel method which increases robustness by adding a coarse alignment of the datasets’ principal components and batchbased point inclusions for the UKF -- Experiments with simulated scans with full coverage of a single vertebra show the method’s capability and accuracy to correct misalignments as large as and 90 mm -- Furthermore, the method registers datasets with varying degrees of missing data and datasets with outlier points coming from adjacent vertebraeÍtem Sensitivity analysis of optimized curve fitting to uniform-noise point samples(2012-05) Ruíz, Óscar; Cortes, Camilo; Acosta, Diego; Aristizábal, Mauricio; Universidad EAFIT. Departamento de Ingeniería Mecánica; Laboratorio CAD/CAM/CAECurve reconstruction from noisy point samples is needed for surface reconstruction in many applications (e.g. medical imaging, reverse engineering,etc.) -- Because of the sampling noise, curve reconstruction is conducted by minimizing the fitting error (f), for several degrees of continuity (usually C0, C1 and C2) -- Previous works involving smooth curves lack the formal assessment of the effect on optimized curve reconstruction of several inputs such as number of control points (m), degree of the parametric curve (p), composition of the knot vector (U), and degree of the norm (k) to calculate the penalty function (f) -- In response to these voids, this article presents a sensitivity analysis of the effect of mand k on f -- We found that the geometric goodness of the fitting (f) is much more sensitive to m than to k -- Likewise, the topological faithfulness on the curve fit is strongly dependent on m -- When an exaggerate number of control points is used, the resulting curve presents spurious loops, curls and peaks, not present in the input data -- We introduce in this article the spectral (frequency) analysis of the derivative of the curve fit as a means to reject fitted curves with spurious curls and peaks -- Large spikes in the derivative signal resemble Kronecker or Dirac Delta functions, which flatten the frequency content adinfinitum -- Ongoing work includes the assessment of the effect of curve degree p on f for non-Nyquist point samples