Search results for "computational geometry"

showing 10 items of 139 documents

Historical Notes on Star Geometry in Mathematics, Art and Nature

2018

Gamma: “I can. Look at this Counterexample 3: a star-polyhedron I shall call it urchin. This consists of 12 star-pentagons. It has 12 vertices, 30 edges, and 12 pentagonal faces-you may check it if you like by counting. Thus the Descartes-Euler thesis is not true at all, since for this polyhedron \(V - E + F = - 6\)”. Delta: “Why do you think that your ‘urchin’ is a polyhedron?” Gamma: “Do you not see? This is a polyhedron, whose faces are the twelve star-pentagons”. Delta: “But then you do not even know what a polygon is! A star-pentagon is certainly not a polygon!”

CombinatoricsPolyhedronMathematics::History and OverviewPolygonMathematics::Metric GeometryComputer Science::Computational GeometryStar (graph theory)History of Mathematics Star polygons and polyhedra.MathematicsCounterexample
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Generic attribute deviation metric for assessing mesh simplification algorithm quality

2002

International audience; This paper describes an efficient method to compare two triangular meshes. Meshes considered here contain geometric features as well as other surface attributes such as material colors, texture, temperature, radiation, etc. Two deviation measurements are presented to assess the differences between two meshes. The first measurement, called geometric deviation, returns geometric differences. The second measurement , called attribute deviation, returns attribute differences regardless of the attribute type. In this paper we present an application of this method to the Mesh Simplification Algorithm (MSA) quality assessment according to the appearance attributes. This ass…

Computationmedia_common.quotation_subjectFeature extraction[INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR]02 engineering and technologySolid modeling[INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG]Computer graphics[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]0202 electrical engineering electronic engineering information engineeringQuality (business)Polygon meshComputingMethodologies_COMPUTERGRAPHICSmedia_commonMathematicsbusiness.industry[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]020207 software engineeringPattern recognitionComputational geometry[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR][INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG]Metric (mathematics)020201 artificial intelligence & image processingArtificial intelligencebusinessAlgorithmProceedings. International Conference on Image Processing
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Approximation of functions over manifolds : A Moving Least-Squares approach

2021

We present an algorithm for approximating a function defined over a $d$-dimensional manifold utilizing only noisy function values at locations sampled from the manifold with noise. To produce the approximation we do not require any knowledge regarding the manifold other than its dimension $d$. We use the Manifold Moving Least-Squares approach of (Sober and Levin 2016) to reconstruct the atlas of charts and the approximation is built on-top of those charts. The resulting approximant is shown to be a function defined over a neighborhood of a manifold, approximating the originally sampled manifold. In other words, given a new point, located near the manifold, the approximation can be evaluated…

Computational Geometry (cs.CG)FOS: Computer and information sciencesComputer Science - Machine LearningClosed manifolddimension reductionMachine Learning (stat.ML)010103 numerical & computational mathematicsComplex dimensionTopology01 natural sciencesMachine Learning (cs.LG)Volume formComputer Science - GraphicsStatistics - Machine Learningmanifold learningApplied mathematics0101 mathematicsfunktiotMathematicsManifold alignmentAtlas (topology)Applied Mathematicshigh dimensional approximationManifoldGraphics (cs.GR)Statistical manifold010101 applied mathematicsregression over manifoldsComputational Mathematicsout-of-sample extensionComputer Science - Computational Geometrynumeerinen analyysimonistotapproksimointimoving least-squaresCenter manifold
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Fully Automatic Trunk Packing with Free Placements

2010

We present a new algorithm to compute the volume of a trunk according to the SAE J1100 standard. Our new algorithm uses state-of-the-art methods from computational geometry and from combinatorial optimization. It finds better solutions than previous approaches for small trunks.

Computational Geometry (cs.CG)FOS: Computer and information sciencesDiscrete Mathematics (cs.DM)ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSComputer Science - Data Structures and AlgorithmsComputer Science - Computational GeometryData Structures and Algorithms (cs.DS)Computer Science - Discrete Mathematics
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Topology-based goodness-of-fit tests for sliced spatial data

2023

In materials science and many other application domains, 3D information can often only be extrapolated by taking 2D slices. In topological data analysis, persistence vineyards have emerged as a powerful tool to take into account topological features stretching over several slices. In the present paper, we illustrate how persistence vineyards can be used to design rigorous statistical hypothesis tests for 3D microstructure models based on data from 2D slices. More precisely, by establishing the asymptotic normality of suitable longitudinal and cross-sectional summary statistics, we devise goodness-of-fit tests that become asymptotically exact in large sampling windows. We illustrate the test…

Computational Geometry (cs.CG)FOS: Computer and information sciencesStatistics and ProbabilityGoodness-of-fit testsApplied MathematicsTopological data analysisPersistence diagramMathematics - Statistics TheoryStatistics Theory (math.ST)VineyardsMaterials scienceComputational MathematicsComputational Theory and Mathematics60F05Topological data analysis Persistence diagram Materials science Vineyards Goodness-of-fit tests Asymptotic normalityFOS: MathematicsAlgebraic Topology (math.AT)Computer Science - Computational GeometryAsymptotic normalityMathematics - Algebraic TopologyComputational Statistics & Data Analysis
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On the reducibility of geometric constraint graphs

2018

Geometric modeling by constraints, whose applications are of interest to communities from various fields such as mechanical engineering, computer aided design, symbolic computation or molecular chemistry, is now integrated into standard modeling tools. In this discipline, a geometric form is specified by the relations that the components of this form must verify instead of explicitly specifying these components. The purpose of the resolution is to deduce the form satisfying all these constraints. Various methods have been proposed to solve this problem. We will focus on the so-called graph-based or graph-based methods with application to the two-dimensional space.

Computational Geometry (cs.CG)FOS: Computer and information sciences[ INFO.INFO-CG ] Computer Science [cs]/Computational Geometry [cs.CG]Computer Science - Computational Geometry[INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG]
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Optimal rates of convergence for persistence diagrams in Topological Data Analysis

2013

Computational topology has recently known an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field. In this paper, we study topological persistence in general metric spaces, with a statistical approach. We show that the use of persistent homology can be naturally considered in general statistical frameworks and persistence diagrams can be used as statistics with interesting convergence properties. Some numerical experiments are performed in various contexts to illustrate our results.

Computational Geometry (cs.CG)FOS: Computer and information sciences[ MATH.MATH-GT ] Mathematics [math]/Geometric Topology [math.GT][STAT.TH] Statistics [stat]/Statistics Theory [stat.TH]Topological Data analysis Persistent homology minimax convergence rates geometric complexes metric spacesGeometric Topology (math.GT)Mathematics - Statistics TheoryStatistics Theory (math.ST)[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG][STAT.TH]Statistics [stat]/Statistics Theory [stat.TH][INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG][ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH][ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG]Machine Learning (cs.LG)Computer Science - LearningMathematics - Geometric Topology[INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG][INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][MATH.MATH-GT]Mathematics [math]/Geometric Topology [math.GT]FOS: Mathematics[ INFO.INFO-CG ] Computer Science [cs]/Computational Geometry [cs.CG]Computer Science - Computational Geometry[MATH.MATH-GT] Mathematics [math]/Geometric Topology [math.GT]
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Computation of the topological type of a real Riemann surface

2012

We present an algorithm for the computation of the topological type of a real compact Riemann surface associated to an algebraic curve, i.e., its genus and the properties of the set of fixed points of the anti-holomorphic involution $\tau$, namely, the number of its connected components, and whether this set divides the surface into one or two connected components. This is achieved by transforming an arbitrary canonical homology basis to a homology basis where the $\mathcal{A}$-cycles are invariant under the anti-holomorphic involution $\tau$.

Computational Geometry (cs.CG)FOS: Computer and information sciencesreal Riemann surface[MATH.MATH-AG] Mathematics [math]/Algebraic Geometry [math.AG]homology basis[ MATH.MATH-AG ] Mathematics [math]/Algebraic Geometry [math.AG]Mathematics - Algebraic Geometryreal algebraic curveholomorphic differentialsFOS: MathematicsComputer Science - Computational Geometryreal ovals[MATH.MATH-AG]Mathematics [math]/Algebraic Geometry [math.AG]Algebraic Geometry (math.AG)
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Efficiently using connectivity information between triangles in a mesh for real-time rendering

2004

Triangle meshes are the most popular standard model used to represent polygonal surfaces. Drawing these meshes as a set of independent triangles involves sending a vast amount of information to the graphics system. Taking advantage of the connectivity information between the triangles in a mesh dramatically diminishes the amount of information the graphics system must handle. Multiresolution Triangle Strips (MTS) represent a triangle mesh as a collection of multiresolution triangles strips. These strips are the basis of both the storage and the rendering stage. The coherence between the extraction of two levels of detail is used in the model in order to decrease the visualisation time.

Computer Networks and CommunicationsComputer scienceComputer Science::Computational GeometryReal-time renderingVisualizationRendering (computer graphics)Computer Science::GraphicsHardware and ArchitectureComputer graphics (images)Triangle meshPolygonPolygon meshGraphicsInteractive visualizationSoftwareLevel of detailComputingMethodologies_COMPUTERGRAPHICSFuture Generation Computer Systems
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The Local Fractional Derivative of Fractal Curves

2008

Fractal curves described by iterated function system (IFS) are generally non-integer derivative. For that we use fractional derivative to investigate differentiability of this curves. We propose a method to calculate local fractional derivative of a curve from IFS property. Also we give some examples of IFS representing the slopes of the right and left half-tangent of the fractal curves.

Computer Science::GraphicsIterated function systemFractalFractal derivativeGeneralizations of the derivativeMathematical analysisAstrophysics::Instrumentation and Methods for AstrophysicsDerivativeDifferentiable functionComputational geometryMathematicsFractional calculus2008 IEEE International Conference on Signal Image Technology and Internet Based Systems
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