Name: Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis With Applications (Springerbriefs in Statistics) (en Inglés)
SKU: 50881034
Price: 59.56 USD
Availability: InStock
Author: Chiara Brombin; Luigi Salmaso; Lara Fontanella
ISBN: 9783319263106

portada Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis With Applications (Springerbriefs in Statistics) (en Inglés)

Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis With Applications (Springerbriefs in Statistics) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Chiara Brombin; Luigi Salmaso; Lara Fontanella

Año

2016

Idioma

Inglés

N° páginas

128

Encuadernación

Tapa Blanda

ISBN13

9783319263106

N° edición

Categorías

Software de matemáticas y estadísticas

Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis With Applications (Springerbriefs in Statistics) (en Inglés)

Chiara Brombin; Luigi Salmaso; Lara Fontanella (Autor) · Springer · Tapa Blanda

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Reseña del libro "Parametric and Nonparametric Inference for Statistical Dynamic Shape Analysis With Applications (Springerbriefs in Statistics) (en Inglés)"

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.