Parametric And Nonparametric Inference For Statistical Dynamic Shape Analysis With Applications (springerbriefs In Statistics)
by Luigi Salmaso /
2016 / English / PDF
3.7 MB Download
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.
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 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.
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.
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.