Statistical Modeling Using Local Gaussian Approximation

Statistical Modeling Using Local Gaussian Approximation
Author: Dag Tjostheim,Håkon Otneim,Bård Stove
Publsiher: Academic Press
Total Pages: 458
Release: 2021-10-05
ISBN 10: 0128154454
ISBN 13: 9780128154458
Language: EN, FR, DE, ES & NL

Statistical Modeling Using Local Gaussian Approximation Book Review:

Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the most well-known and most used distribution in statistics, to a large class of non-Gaussian and nonlinear situations through local approximation. This extension enables the reader to follow new methods in assessing dependence and conditional dependence, in estimating probability and spectral density functions, and in discrimination. Chapters in this release cover Parametric, nonparametric, locally parametric, Dependence, Local Gaussian correlation and dependence, Local Gaussian correlation and the copula, Applications in finance, and more. Additional chapters explores Measuring dependence and testing for independence, Time series dependence and spectral analysis, Multivariate density estimation, Conditional density estimation, The local Gaussian partial correlation, Regression and conditional regression quantiles, and a A local Gaussian Fisher discriminant. Reviews local dependence modeling with applications to time series and finance markets Introduces new techniques for density estimation, conditional density estimation, and tests of conditional independence with applications in economics Evaluates local spectral analysis, discovering hidden frequencies in extremes and hidden phase differences Integrates textual content with three useful R packages

Handbook of Research on Cloud Computing and Big Data Applications in IoT

Handbook of Research on Cloud Computing and Big Data Applications in IoT
Author: Gupta, B. B.,Agrawal, Dharma P.
Publsiher: IGI Global
Total Pages: 609
Release: 2019-04-12
ISBN 10: 1522584080
ISBN 13: 9781522584087
Language: EN, FR, DE, ES & NL

Handbook of Research on Cloud Computing and Big Data Applications in IoT Book Review:

Today, cloud computing, big data, and the internet of things (IoT) are becoming indubitable parts of modern information and communication systems. They cover not only information and communication technology but also all types of systems in society including within the realms of business, finance, industry, manufacturing, and management. Therefore, it is critical to remain up-to-date on the latest advancements and applications, as well as current issues and challenges. The Handbook of Research on Cloud Computing and Big Data Applications in IoT is a pivotal reference source that provides relevant theoretical frameworks and the latest empirical research findings on principles, challenges, and applications of cloud computing, big data, and IoT. While highlighting topics such as fog computing, language interaction, and scheduling algorithms, this publication is ideally designed for software developers, computer engineers, scientists, professionals, academicians, researchers, and students.

Biomedical Image Segmentation

Biomedical Image Segmentation
Author: Ayman El-Baz,Xiaoyi Jiang,Jasjit S. Suri
Publsiher: CRC Press
Total Pages: 526
Release: 2016-11-17
ISBN 10: 1482258560
ISBN 13: 9781482258561
Language: EN, FR, DE, ES & NL

Biomedical Image Segmentation Book Review:

As one of the most important tasks in biomedical imaging, image segmentation provides the foundation for quantitative reasoning and diagnostic techniques. A large variety of different imaging techniques, each with its own physical principle and characteristics (e.g., noise modeling), often requires modality-specific algorithmic treatment. In recent years, substantial progress has been made to biomedical image segmentation. Biomedical image segmentation is characterized by several specific factors. This book presents an overview of the advanced segmentation algorithms and their applications.

Probabilistic Finite Element Model Updating Using Bayesian Statistics

Probabilistic Finite Element Model Updating Using Bayesian Statistics
Author: Tshilidzi Marwala,Ilyes Boulkaibet,Sondipon Adhikari
Publsiher: John Wiley & Sons
Total Pages: 248
Release: 2016-09-23
ISBN 10: 111915300X
ISBN 13: 9781119153009
Language: EN, FR, DE, ES & NL

Probabilistic Finite Element Model Updating Using Bayesian Statistics Book Review:

Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure. The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering. Key features: Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students. Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations. The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.

Surrogates

Surrogates
Author: Robert B. Gramacy
Publsiher: CRC Press
Total Pages: 560
Release: 2020-03-10
ISBN 10: 1000766527
ISBN 13: 9781000766523
Language: EN, FR, DE, ES & NL

Surrogates Book Review:

Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc.

Introduction to Bayesian Methods in Ecology and Natural Resources

Introduction to Bayesian Methods in Ecology and Natural Resources
Author: Edwin J. Green,Andrew O. Finley,William E. Strawderman
Publsiher: Springer Nature
Total Pages: 183
Release: 2020-11-26
ISBN 10: 303060750X
ISBN 13: 9783030607500
Language: EN, FR, DE, ES & NL

Introduction to Bayesian Methods in Ecology and Natural Resources Book Review:

This book presents modern Bayesian analysis in a format that is accessible to researchers in the fields of ecology, wildlife biology, and natural resource management. Bayesian analysis has undergone a remarkable transformation since the early 1990s. Widespread adoption of Markov chain Monte Carlo techniques has made the Bayesian paradigm the viable alternative to classical statistical procedures for scientific inference. The Bayesian approach has a number of desirable qualities, three chief ones being: i) the mathematical procedure is always the same, allowing the analyst to concentrate on the scientific aspects of the problem; ii) historical information is readily used, when appropriate; and iii) hierarchical models are readily accommodated. This monograph contains numerous worked examples and the requisite computer programs. The latter are easily modified to meet new situations. A primer on probability distributions is also included because these form the basis of Bayesian inference. Researchers and graduate students in Ecology and Natural Resource Management will find this book a valuable reference.

Computational Statistics in Data Science

Computational Statistics in Data Science
Author: Walter W. Piegorsch,Richard A. Levine,Hao Helen Zhang,Thomas C. M. Lee
Publsiher: John Wiley & Sons
Total Pages: 672
Release: 2022-03-23
ISBN 10: 1119561086
ISBN 13: 9781119561088
Language: EN, FR, DE, ES & NL

Computational Statistics in Data Science Book Review:

An essential roadmap to the application of computational statistics in contemporary data science In Computational Statistics in Data Science, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques. Computational Statistics in Data Science provides complimentary access to finalized entries in the Wiley StatsRef: Statistics Reference Online compendium. Readers will also find: A thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas Comprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learning Perfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, Computational Statistics in Data Science will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.

Handbook of Statistics

Handbook of Statistics
Author: Anonim
Publsiher: Elsevier
Total Pages: 776
Release: 2012-05-18
ISBN 10: 0444538631
ISBN 13: 9780444538635
Language: EN, FR, DE, ES & NL

Handbook of Statistics Book Review:

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respective areas

Uncertainty Quantification in Multiscale Materials Modeling

Uncertainty Quantification in Multiscale Materials Modeling
Author: Yan Wang,David L. McDowell
Publsiher: Woodhead Publishing Limited
Total Pages: 900
Release: 2020-03-12
ISBN 10: 0081029411
ISBN 13: 9780081029411
Language: EN, FR, DE, ES & NL

Uncertainty Quantification in Multiscale Materials Modeling Book Review:

Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales.

Essays in Nonlinear Time Series Econometrics

Essays in Nonlinear Time Series Econometrics
Author: Niels Haldrup,Mika Meitz,Pentti Saikkonen
Publsiher: Oxford University Press
Total Pages: 352
Release: 2014-05
ISBN 10: 0199679959
ISBN 13: 9780199679959
Language: EN, FR, DE, ES & NL

Essays in Nonlinear Time Series Econometrics Book Review:

This edited collection concerns nonlinear economic relations that involve time. It is divided into four broad themes that all reflect the work and methodology of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics. The themes are: Testing for linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial econometrics. All these research fields include contributions that represent state of the art in econometrics such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general to specific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependence for asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Teräsvirta has had and will continue to have, on the profession.

Advances in Computational Modeling and Simulation

Advances in Computational Modeling and Simulation
Author: Rallapalli Srinivas,Rajesh Kumar,Mainak Dutta
Publsiher: Springer Nature
Total Pages: 243
Release: 2022
ISBN 10: 981167857X
ISBN 13: 9789811678578
Language: EN, FR, DE, ES & NL

Advances in Computational Modeling and Simulation Book Review:

The book presents select proceedings of Global meet on Computational Modelling and Simulation, Recent Innovations, Challenges and Perspectives, 2020. This book covers leading-edge technologies from different domains such as computation in optimization and control, multiscale and multiphysics modeling and computation analysis, environmental modeling, modeling approaches to enterprise systems and services, finite element analysis, dependability and security, high-performance computation/cloud computing applications, computational biology and chemistry and computational mechanics. The primary goal of this book is to strengthen pre-eminence in computational modeling and simulation by catalyzing the transformative use of innovative developments in a wide range of disciplines to achieve lasting societal impact. The book discusses on how to perform simulation of large complex dynamic systems in an efficient manner using advanced computational analysis. The inter-disciplinary nature of the book would be a valuable reference for academicians and research scientists, industrialists interested in modelling and simulation driven by computational technology.

Handbook of Parallel Computing and Statistics

Handbook of Parallel Computing and Statistics
Author: Erricos John Kontoghiorghes
Publsiher: CRC Press
Total Pages: 552
Release: 2005-12-21
ISBN 10: 9781420028683
ISBN 13: 1420028685
Language: EN, FR, DE, ES & NL

Handbook of Parallel Computing and Statistics Book Review:

Technological improvements continue to push back the frontier of processor speed in modern computers. Unfortunately, the computational intensity demanded by modern research problems grows even faster. Parallel computing has emerged as the most successful bridge to this computational gap, and many popular solutions have emerged based on its concepts

Intelligence Science and Big Data Engineering Visual Data Engineering

Intelligence Science and Big Data Engineering  Visual Data Engineering
Author: Zhen Cui,Jinshan Pan,Shanshan Zhang,Liang Xiao,Jian Yang
Publsiher: Springer Nature
Total Pages: 577
Release: 2019-11-28
ISBN 10: 3030361896
ISBN 13: 9783030361891
Language: EN, FR, DE, ES & NL

Intelligence Science and Big Data Engineering Visual Data Engineering Book Review:

The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.

Computational Photography

Computational Photography
Author: Rastislav Lukac
Publsiher: CRC Press
Total Pages: 564
Release: 2017-12-19
ISBN 10: 1439817502
ISBN 13: 9781439817506
Language: EN, FR, DE, ES & NL

Computational Photography Book Review:

Computational photography refers broadly to imaging techniques that enhance or extend the capabilities of digital photography. This new and rapidly developing research field has evolved from computer vision, image processing, computer graphics and applied optics—and numerous commercial products capitalizing on its principles have already appeared in diverse market applications, due to the gradual migration of computational algorithms from computers to imaging devices and software. Computational Photography: Methods and Applications provides a strong, fundamental understanding of theory and methods, and a foundation upon which to build solutions for many of today's most interesting and challenging computational imaging problems. Elucidating cutting-edge advances and applications in digital imaging, camera image processing, and computational photography, with a focus on related research challenges, this book: Describes single capture image fusion technology for consumer digital cameras Discusses the steps in a camera image processing pipeline, such as visual data compression, color correction and enhancement, denoising, demosaicking, super-resolution reconstruction, deblurring, and high dynamic range imaging Covers shadow detection for surveillance applications, camera-driven document rectification, bilateral filtering and its applications, and painterly rendering of digital images Presents machine-learning methods for automatic image colorization and digital face beautification Explores light field acquisition and processing, space-time light field rendering, and dynamic view synthesis with an array of cameras Because of the urgent challenges associated with emerging digital camera applications, image processing methods for computational photography are of paramount importance to research and development in the imaging community. Presenting the work of leading experts, and edited by a renowned authority in digital color imaging and camera image processing, this book considers the rapid developments in this area and addresses very particular research and application problems. It is ideal as a stand-alone professional reference for design and implementation of digital image and video processing tasks, and it can also be used to support graduate courses in computer vision, digital imaging, visual data processing, and computer graphics, among others.

Phase Transitions and Renormalization Group

Phase Transitions and Renormalization Group
Author: Jean Zinn-Justin
Publsiher: Oxford University Press on Demand
Total Pages: 452
Release: 2007-07-05
ISBN 10: 0199227195
ISBN 13: 9780199227198
Language: EN, FR, DE, ES & NL

Phase Transitions and Renormalization Group Book Review:

No further information has been provided for this title.

New Advances in Statistics and Data Science

New Advances in Statistics and Data Science
Author: Ding-Geng Chen,Zhezhen Jin,Gang Li,Yi Li,Aiyi Liu,Yichuan Zhao
Publsiher: Springer
Total Pages: 348
Release: 2018-01-17
ISBN 10: 3319694162
ISBN 13: 9783319694160
Language: EN, FR, DE, ES & NL

New Advances in Statistics and Data Science Book Review:

This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.

Knowledge Incorporation in Evolutionary Computation

Knowledge Incorporation in Evolutionary Computation
Author: Yaochu Jin
Publsiher: Springer Science & Business Media
Total Pages: 548
Release: 2004-10-20
ISBN 10: 9783540229025
ISBN 13: 3540229027
Language: EN, FR, DE, ES & NL

Knowledge Incorporation in Evolutionary Computation Book Review:

Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu tionary search, into evolutionary algorithms has received increasing interest in the recent years. It has been shown from various motivations that knowl edge incorporation into evolutionary search is able to significantly improve search efficiency. However, results on knowledge incorporation in evolution ary computation have been scattered in a wide range of research areas and a systematic handling of this important topic in evolutionary computation still lacks. This edited book is a first attempt to put together the state-of-art and re cent advances on knowledge incorporation in evolutionary computation within a unified framework. Existing methods for knowledge incorporation are di vided into the following five categories according to the functionality of the incorporated knowledge in the evolutionary algorithms. 1. Knowledge incorporation in representation, population initialization, - combination and mutation. 2. Knowledge incorporation in selection and reproduction. 3. Knowledge incorporation in fitness evaluations. 4. Knowledge incorporation through life-time learning and human-computer interactions. 5. Incorporation of human preferences in multi-objective evolutionary com putation. The intended readers of this book are graduate students, researchers and practitioners in all fields of science and engineering who are interested in evolutionary computation. The book is divided into six parts. Part I contains one introductory chapter titled "A selected introduction to evolutionary computation" by Yao, which presents a concise but insightful introduction to evolutionary computation.

Computational Science ICCS 2021

Computational Science     ICCS 2021
Author: Maciej Paszynski,Dieter Kranzlmüller,Valeria V. Krzhizhanovskaya,Jack J. Dongarra,Peter M.A. Sloot
Publsiher: Springer Nature
Total Pages: 592
Release: 2021-06-09
ISBN 10: 3030779777
ISBN 13: 9783030779771
Language: EN, FR, DE, ES & NL

Computational Science ICCS 2021 Book Review:

The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually.

Uncertainty Quantification and Predictive Computational Science

Uncertainty Quantification and Predictive Computational Science
Author: Ryan G. McClarren
Publsiher: Springer
Total Pages: 345
Release: 2018-11-23
ISBN 10: 3319995251
ISBN 13: 9783319995250
Language: EN, FR, DE, ES & NL

Uncertainty Quantification and Predictive Computational Science Book Review:

This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.

Medical Image Computing and Computer Assisted Intervention MICCAI 2000

Medical Image Computing and Computer Assisted Intervention   MICCAI 2000
Author: Scott L. Delp,Anthony M. DiGoia,Branislav Jaramaz
Publsiher: Springer
Total Pages: 1254
Release: 2004-02-12
ISBN 10: 3540408991
ISBN 13: 9783540408994
Language: EN, FR, DE, ES & NL

Medical Image Computing and Computer Assisted Intervention MICCAI 2000 Book Review:

This book constitutes the refereed proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2000, held in Pittsburgh, PA, USA in October 2000.The 136 papers presented were carefully reviewed and selected from a total of 194 submissions. The book offers topical sections on neuroimaging and neuroscience, segmentation, oncology, medical image analysis and visualization, registration, surgical planning and simulation, endoscopy and laparoscopy, cardiac image analysis, vascular image analysis, visualization, surgical navigation, medical robotics, plastic and craniofacial surgery, and orthopaedics.