Robust range image registration [[electronic resource] ] : using genetic algorithms and the surface interpenetration measure / / Luciano Silva and Olga R.P. Bellon, Kim L. Boyer

• Record Nr.: UNINA9910450177003321
• Autore: Silva Luciano Afonso da <1973->
• Pubbl/distr/stampa: Hackensack, N.J., : World Scientific, c2005
• ISBN: 1-281-87674-7; 9786611876746; 981-256-312-1
• Descrizione fisica: 1 online resource (173 p.)
• Collana: Series in machine perception and artificial intelligence ; ; v. 60
• Altri autori (Persone): BellonOlga R. P; BoyerKim L
• Disciplina: 003/.3
• Soggetti: Computer simulation; Mathematical models; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. 157-162) and index.
• Nota di contenuto: Preface; Contents; Introduction; Range Image Registration; Surface Interpenetration Measure (SIM); Range Image Registration using Genetic Algorithms; Robust Range Registration by Robust Range Registration by; Multiview Range Image Registration; Closing Comments; Appendix - Experimental Results; Bibliography; Index
• Sommario/riassunto: This book addresses the range image registration problem for automatic 3D model construction. The focus is on obtaining highly precise alignments between different view pairs of the same object to avoid 3D model distortions; in contrast to most prior work, the view pairs may exhibit relatively little overlap and need not be prealigned.

Modern Engineering Statistics [[electronic resource]]

• Record Nr.: UNINA9910144722203321
• Autore: Ryan Thomas P
• Pubbl/distr/stampa: Hoboken, : Wiley, 2007
• ISBN: 1-281-09412-9; 9786611094126; 0-470-12844-5; 0-470-12843-7
• Descrizione fisica: 1 online resource (608 p.)
• Disciplina: 519.5; 620.0072
• Soggetti: Engineering - Statistical methods; Engineering; Engineering & Applied Sciences; Applied Mathematics; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di contenuto: Modern Engineering Statistics; Contents; Preface; 1. Methods of Collecting and Presenting Data; 1.1 Observational Data and Data from Designed Experiments; 1.2 Populations and Samples; 1.3 Variables; 1.4 Methods of Displaying Small Data Sets; 1.4.1 Stem-and-Leaf Display; 1.4.2 Time Sequence Plot and Control Chart; 1.4.3 Lag Plot; 1.4.4 Scatter Plot; 1.4.5 Digidot Plot; 1.4.6 Dotplot; 1.5 Methods of Displaying Large Data Sets; 1.5.1 Histogram; 1.5.2 Boxplot; 1.6 Outliers; 1.7 Other Methods; 1.8 Extremely Large Data Sets: Data Mining; 1.9 Graphical Methods: Recommendations; 1.10 Summary; ReferencesExercises; 2. Measures of Location and Dispersion; 2.1 Estimating Location Parameters; 2.2 Estimating Dispersion Parameters; 2.3 Estimating Parameters from Grouped Data; 2.4 Estimates from a Boxplot; 2.5 Computing Sample Statistics with MINITAB; 2.6 Summary; Reference; Exercises; 3. Probability and Common Probability Distributions; 3.1 Probability: From the Ethereal to the Concrete; 3.1.1 ; Manufacturing Applications; 3.2 Probability Concepts and Rules; 3.2.1 Extension to Multiple Events; 3.2.1.1 Law of Total Probability and Bayes' Theorem; 3.3 Common Discrete Distributions; 3.3.1 Expected Value and Variance3.3.2 Binomial Distribution; 3.3.2.1 Testing for the Appropriateness of the Binomial Model; 3.3.3 Hypergeometric Distribution; 3.3.4 Poisson Distribution; 3.3.4.1 Testing for the Appropriateness of the Poisson Model; 3.3.5 Geometric Distribution; 3.4 Common Continuous Distributions; 3.4.1 Expected Value and Variance; 3.4.2 Determining Probabilities for Continuous Random Variables; 3.4.3 Normal Distribution; 3.4.3.1 Software-Aided Normal Probability Computations; 3.4.3.2 Testing the Normality Assumption; 3.4.4 t-Distribution; 3.4.5 Gamma Distribution; 3.4.5.1 Chi-Square Distribution3.4.5.2 Exponential Distribution; 3.4.6 Weibull Distribution; 3.4.7 Smallest Extreme Value Distribution; 3.4.8 Lognormal Distribution; 3.4.9 F Distribution; 3.5 General Distribution Fitting; 3.6 How to Select a Distribution; 3.7 Summary; References; Exercises; 4. Point Estimation; 4.1 Point Estimators and Point Estimates; 4.2 Desirable Properties of Point Estimators; 4.2.1 Unbiasedness and Consistency; 4.2.2 Minimum Variance; 4.2.3 Estimators Whose Properties Depend on the Assumed Distribution; 4.2.4 Comparing Biased and Unbiased Estimators; 4.3 Distributions of Sampling Statistics4.3.1 Central Limit Theorem; 4.3.1.1 Illustration of Central Limit Theorem; 4.3.2 Statistics with Nonnormal Sampling Distributions; 4.4 Methods of Obtaining Estimators; 4.4.1 Method of Maximum Likelihood; 4.4.2 Method of Moments; 4.4.3 Method of Least Squares; 4.5 Estimating σ; 4.6 Estimating Parameters Without Data; 4.7 Summary; References; Exercises; 5. Confidence Intervals and Hypothesis Tests-One Sample; 5.1 Confidence Interval for μ: Normal Distribution, σ Not Estimated from Sample Data; 5.1.1 Sample Size Determination; 5.1.2 Interpretation and Use; 5.1.3 General Form of Confidence Intervals
• Sommario/riassunto: An introductory perspective on statistical applications in the field of engineering Modern Engineering Statistics presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering. With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a