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Uncertainty in Geometric Computations (The Springer International Series in Engineering and Computer Science)
 
 
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Uncertainty in Geometric Computations (The Springer International Series in Engineering and Computer Science) [Hardcover]

Joab Winkler (Editor), Mahesan Niranjan (Editor)

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Book Description

079237309X 978-0792373094 October 31, 2002 1
Computer simulations and modelling are used frequently in science and engineering, in applications ranging from the understanding of natural and artificial phenomena to the design, test and manufacturing stages of production. This widespread use necessarily implies that a detailed knowledge of the limitations of computer simulations is required. In particular, the usefulness of a computer simulation is directly dependent on the user's knowledge of the uncertainty in the simulation. Typical limitations of computer simulations include uncertainty in the data, parameter uncertainty, errors in the initial data, modelling errors, unmodelled phenomena, reduced order models, and approximations and numerical errors.
Although an improvement in the physical understanding of the phenomena being modelled is an important requirement of a good computer simulation, the simulation will be plagued by deficiencies if the limitations listed above are not considered when analyzing its results. Since uncertainties can never be completely eliminated, they must be quantified and their propagation through the computations must be considered. The uses of computer modelling are diverse, and one particular application, the effect of uncertainty in geometric computations, is considered in this book. In particular, geometric computations occur extensively in geometric modelling, computer vision, computer graphics and pattern recognition.
Uncertainty in Geometric Computations contains the proceedings of a workshop that was held in Sheffield, United Kingdom, in which the management and assessment of uncertainty in geometric computations was considered. The theme that unites these four subject areas is the requirement to perform computations on real geometric data, which (i) may have errors, for example, the tolerance of a coordinate measuring machine that is used in reverse engineering, and/or (ii) is incomplete because of occlusion, which may occur in computer vision, for example, a face recognition system. These characteristics of real geometric data impose tight constraints on the methods and algorithms that are used for their processing and interrogation, and this workshop provided a forum for their discussion.
One of the novel features of the workshop was the wide background of the audience and invited speakers &endash; applied mathematicians, computer scientists and engineers &endash; and this provided a forum for the establishment of new collaborative links between mathematicians and engineers, thereby emphasizing the interdisciplinary nature of the many outstanding problems.

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Inside This Book (learn more)
First Sentence:
The svLIS CSG geometric modeller [1] represents solids using implicit inequalities at the leaves of a set-theoretic operator tree. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
companion matrix resultant, latent space centres, ordinary interval arithmetic, linear interval estimations, feature localization error, using affine arithmetic, power basis polynomials, floating point environment, affine intervals, conformal space, scatterometer observations, synthetic visual speech, exact geometric computation, line attractor, resultant matrices, conformal model, resultant matrix, incidence test, geometric modeller, boundary evaluation, normalised distance, generative topographic mapping, affine form, numerical condition, geometric product
Key Phrases - Capitalized Phrases (CAPs): (learn more)
United Kingdom, Dinesh Manocha, John Keyser, New York, Tim Culver, Shankar Krishnan, Department of Computer Science, Chapel Hill, International Conference, Monte Carlo, Morgan Kaufmann, Neural Computation, University of North Carolina, Annual Conference Series, Bradley Fighting Vehicle, Los Angeles, Oxford University Press, Regularization Networks, San Mateo
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