The Experts below are selected from a list of 8451 Experts worldwide ranked by ideXlab platform
Suresh Venkatasubramanian - One of the best experts on this subject based on the ideXlab platform.
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Symposium on Computational Geometry - Comparing distributions and shapes using the kernel Distance
Proceedings of the 27th annual ACM symposium on Computational geometry - SoCG '11, 2011Co-Authors: Sarang Joshi, Raj Varma Kommaraji, Jeff M. Phillips, Suresh VenkatasubramanianAbstract:Starting with a similarity function between objects, it is possible to define a Distance metric (the kernel Distance) on pairs of objects, and more generally on probability distributions over them. These Distance metrics have a deep basis in functional analysis and geometric measure theory, and have a rich structure that includes an isometric embedding into a Hilbert space. They have recently been applied to numerous problems in machine learning and shape analysis. SIn this paper, we provide the first algorithmic analysis of these Distance metrics. Our main contributions are as follows: We present fast approximation algorithms for computing the kernel Distance between two Point sets P and Q that runs in near-linear time in the size of P ∪ Q (an explicit calculation would take quadratic time). We present polynomial-time algorithms for approximately minimizing the kernel Distance under rigid transformation; they run in time O(n + poly(1/e, log n)). We provide several general techniques for reducing complex objects to convenient sparse representations (specifically to Point sets or sets of Points sets) which approximately preserve the kernel Distance. In particular, this allows us to reduce problems of computing the kernel Distance between various types of objects such as curves, surfaces, and distributions to computing the kernel Distance between Point sets.
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Shape Modeling International - Approximate shape matching and symmetry detection for 3D shapes with guaranteed error bounds
2009 IEEE International Conference on Shape Modeling and Applications, 2009Co-Authors: Shankar Krishnan, Suresh VenkatasubramanianAbstract:In this paper, we describe a system for approximate shape matching and symmetry (rotation and reflection) detection of geometric shapes represented as Point clouds. Rather than using the leastsquares Distance as a measure of similarity between shapes, we use the Hausdorff Distance between Point sets as the underlying shape metric. This allows us to exploit methods from geometric pattern matching to return symmetries and rigid transformation matches with guaranteed error bounds on the quality of our solution. The approximation is determined by intuitive user-specified input precision and Distance threshold parameters. Another important feature of our method is that it leverages FFT-based techniques for string matching to compute all approximate symmetries simultaneously. Our algorithm is simple to implement and is efficient; we present a detailed experimental study.
Pieter Tibboel - One of the best experts on this subject based on the ideXlab platform.
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Existence of a lower bound for the Distance between Point masses of relative equilibria for generalised quasi-homogeneous n-body problems and the curved n-body problem
Journal of Mathematical Physics, 2015Co-Authors: Pieter TibboelAbstract:We prove that if for relative equilibrium solutions of a generalisation of quasi-homogeneous n-body problems the masses and rotation are given, then the minimum Distance between the Point masses of such a relative equilibrium has a universal lower bound that is not equal to zero. We furthermore prove that the set of such relative equilibria is compact and prove related results for n-body problems in spaces of constant Gaussian curvature.
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Existence of a lower bound for the Distance between Point masses of relative equilibria in $\mathbb{S}^{k-1}$, $k\geq 3$
arXiv: Dynamical Systems, 2014Co-Authors: Pieter TibboelAbstract:We prove that if for the curved $n$-body problem in $\mathbb{S}^{k-1}$, $k\geq 3$, the masses are given, the minimum Distance between the Point masses of a specific type of relative equilibrium solution that is a generalisation of positive elliptic relative equilibria and positive elliptic-elliptic relative equilibria has a universal lower bound that is not equal to zero.
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Existence of a lower bound for the Distance between Point masses of relative equilibria in spaces of constant curvature
arXiv: Dynamical Systems, 2014Co-Authors: Pieter TibboelAbstract:We prove that if for the curved $n$-body problem the masses are given, the minimum Distance between the Point masses of a specific type of relative equilibrium solution to that problem has a universal lower bound that is not equal to zero. We furthermore prove that the set of all such relative equilibria is compact. This class of relative equilibria includes all relative equilibria of the curved $n$-body problem in $\mathbb{S}^{2}$, $\mathbb{H}^{2}$ and a significant subset of the relative equilibria for $\mathbb{S}^{3}$ and $\mathbb{H}^{3}$.
Liao Ping - One of the best experts on this subject based on the ideXlab platform.
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Calculating of Complex Surface Profile Error Based on Subdivision Approach Algorithm and Genetic Algorithm
Journal of Mechanical Engineering, 2010Co-Authors: Liao PingAbstract:The evaluation of complex surface profile error is usually difficult to perform in the field of ultra-precise manufacture and measurement.On the basis of analysis of study status of complex surface profile error,the key problem is proposed,the definition of complex surface profile error is description,and its math model is established.On the basis of analysis of NURBS surface,the subdivision approach algorithm for calculating the minimum Distance between Point and surface is proposed.The contradiction among calculation accuracy,encoding length and computation complexity of standard genetic algorithm is analyzed,the improved genetic algorithm is proposed,its crossover operator and mutation operator are offered.The detailed steps are established for calculating complex surface profile error based on subdivision approach algorithm and genetic algorithm.It can obtain precision result to calculate complex surface profile error by use of genetic algorithm with canonicity real number encoding and subdivision approach algorithm.This method can be realized easily on computer and is very suitable for three coordinate measuring machine.
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Using Particle Swarm Optimization Algorithm to Calculate the Minimum Distance from Point to Complex Surface
Computer Simulation, 2009Co-Authors: Liao PingAbstract:It is a complex nonlinear optimization problem to calculate the minimum Distance from Point to complex surface.This paper expounded the basic theory about particle swarm optimization algorithm,introduced the description of complex surface using NURBS,established a math model of the minimum Distance from Point to complex surface,proposed a method of using particle swarm optimization algorithm to calculate the minimum Distance between Point and complex surface.A series of calculating samples prove that this method is feasible and effective,and it can obtain precise result.This method can realized easily using computer and applied to CAD/CAM and virtual reality.
Haiyan Bai - One of the best experts on this subject based on the ideXlab platform.
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Propensity score interval matching: using bootstrap confidence intervals for accommodating estimation errors of propensity scores.
BMC medical research methodology, 2015Co-Authors: Wei Pan, Haiyan BaiAbstract:Background Propensity score methods have become a popular tool for reducing selection bias in making causal inference from observational studies in medical research. Propensity score matching, a key component of propensity score methods, normally matches units based on the Distance between Point estimates of the propensity scores. The problem with this technique is that it is difficult to establish a sensible criterion to evaluate the closeness of matched units without knowing estimation errors of the propensity scores.
Don H Johnson - One of the best experts on this subject based on the ideXlab platform.
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calculation of the kullback leibler Distance between Point process models
International Conference on Acoustics Speech and Signal Processing, 2001Co-Authors: Charlotte M Gruner, Don H JohnsonAbstract:We have developed a method for quantifying neural response changes in terms of the Kullback-Leibler Distance between the intensity functions for each stimulus condition. We use empirical histogram estimates to characterize the intensity function of the neural response. A critical factor in determining the histogram estimates is selection of bin-width. We analytically derive the Kullback-Leibler Distance between two Poisson processes and two dead time modified Poisson processes in terms of the bin-width selected. Our results show that, for constant intensity processes having the same number of expected counts, the Distance between the dead time modified processes is larger than between the Poisson processes.
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ICASSP - Calculation of the Kullback-Leibler Distance between Point process models
2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 1Co-Authors: Charlotte M Gruner, Don H JohnsonAbstract:We have developed a method for quantifying neural response changes in terms of the Kullback-Leibler Distance between the intensity functions for each stimulus condition. We use empirical histogram estimates to characterize the intensity function of the neural response. A critical factor in determining the histogram estimates is selection of bin-width. We analytically derive the Kullback-Leibler Distance between two Poisson processes and two dead time modified Poisson processes in terms of the bin-width selected. Our results show that, for constant intensity processes having the same number of expected counts, the Distance between the dead time modified processes is larger than between the Poisson processes.