大葉大學圖書館 |

Parametric and nonparametric inference for statistical dynamic shape analysis with applications[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.5
書名/作者:	Parametric and nonparametric inference for statistical dynamic shape analysis with applications/ by Chiara Brombin ... [et al.].
其他作者:	Brombin, Chiara.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	x, 115 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Mathematical statistics.
標題:	Statistics.
標題:	Statistical Theory and Methods.
標題:	Probability and Statistics in Computer Science.
標題:	Computational Mathematics and Numerical Analysis.
ISBN:	9783319263113
ISBN:	9783319263106
內容註:	Part I Offset Normal Distribution for Dynamic Shapes -- Basic Concepts and Definitions -- Shape Inference and the Offset-Normal Distribution -- Dynamic Shape Analysis Through the Offset-Normal Distribution -- Part II Combination-Based Permutation Tests for Shape Analysis -- Parametric and Non-Parametric Testing of Mean Shapes -- Applications of NPC Methodology -- Shape Inference and the Offset-Normal Distribution.
摘要、提要註:	This book considers specific inferential issues arising from the analysis of dynamic shapes with the attempt to solve the problems at hand using probability models and nonparametric tests. The models are simple to understand and interpret and provide a useful tool to describe the global dynamics of the landmark configurations. However, because of the non-Euclidean nature of shape spaces, distributions in shape spaces are not straightforward to obtain. The book explores the use of the Gaussian distribution in the configuration space, with similarity transformations integrated out. Specifically, it works with the offset-normal shape distribution as a probability model for statistical inference on a sample of a temporal sequence of landmark configurations. This enables inference for Gaussian processes from configurations onto the shape space. The book is divided in two parts, with the first three chapters covering material on the offset-normal shape distribution, and the remaining chapters covering the theory of NonParametric Combination (NPC) tests. The chapters offer a collection of applications which are bound together by the theme of this book. They refer to the analysis of data from the FG-NET (Face and Gesture Recognition Research Network) database with facial expressions. For these data, it may be desirable to provide a description of the dynamics of the expressions, or testing whether there is a difference between the dynamics of two facial expressions or testing which of the landmarks are more informative in explaining the pattern of an expression.
電子資源:	http://dx.doi.org/10.1007/978-3-319-26311-3

Parametric and nonparametric inference for statistical dynamic shape analysis with applications[electronic resource] /
Parametric and nonparametric inference for statistical dynamic shape analysis with applications[electronic resource] /by Chiara Brombin ... [et al.]. - Cham :Springer International Publishing :2016. - x, 115 p. :ill., digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

Part I Offset Normal Distribution for Dynamic Shapes -- Basic Concepts and Definitions -- Shape Inference and the Offset-Normal Distribution -- Dynamic Shape Analysis Through the Offset-Normal Distribution -- Part II Combination-Based Permutation Tests for Shape Analysis -- Parametric and Non-Parametric Testing of Mean Shapes -- Applications of NPC Methodology -- Shape Inference and the Offset-Normal Distribution.

This book considers specific inferential issues arising from the analysis of dynamic shapes with the attempt to solve the problems at hand using probability models and nonparametric tests. The models are simple to understand and interpret and provide a useful tool to describe the global dynamics of the landmark configurations. However, because of the non-Euclidean nature of shape spaces, distributions in shape spaces are not straightforward to obtain. The book explores the use of the Gaussian distribution in the configuration space, with similarity transformations integrated out. Specifically, it works with the offset-normal shape distribution as a probability model for statistical inference on a sample of a temporal sequence of landmark configurations. This enables inference for Gaussian processes from configurations onto the shape space. The book is divided in two parts, with the first three chapters covering material on the offset-normal shape distribution, and the remaining chapters covering the theory of NonParametric Combination (NPC) tests. The chapters offer a collection of applications which are bound together by the theme of this book. They refer to the analysis of data from the FG-NET (Face and Gesture Recognition Research Network) database with facial expressions. For these data, it may be desirable to provide a description of the dynamics of the expressions, or testing whether there is a difference between the dynamics of two facial expressions or testing which of the landmarks are more informative in explaining the pattern of an expression.

ISBN: 9783319263113

Standard No.: 10.1007/978-3-319-26311-3doiSubjects--Topical Terms:

171875
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Parametric and nonparametric inference for statistical dynamic shape analysis with applications[electronic resource] /
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520 $a This book considers specific inferential issues arising from the analysis of dynamic shapes with the attempt to solve the problems at hand using probability models and nonparametric tests. The models are simple to understand and interpret and provide a useful tool to describe the global dynamics of the landmark configurations. However, because of the non-Euclidean nature of shape spaces, distributions in shape spaces are not straightforward to obtain. The book explores the use of the Gaussian distribution in the configuration space, with similarity transformations integrated out. Specifically, it works with the offset-normal shape distribution as a probability model for statistical inference on a sample of a temporal sequence of landmark configurations. This enables inference for Gaussian processes from configurations onto the shape space. The book is divided in two parts, with the first three chapters covering material on the offset-normal shape distribution, and the remaining chapters covering the theory of NonParametric Combination (NPC) tests. The chapters offer a collection of applications which are bound together by the theme of this book. They refer to the analysis of data from the FG-NET (Face and Gesture Recognition Research Network) database with facial expressions. For these data, it may be desirable to provide a description of the dynamics of the expressions, or testing whether there is a difference between the dynamics of two facial expressions or testing which of the landmarks are more informative in explaining the pattern of an expression.
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