Permutation tests in shape analysis [electronic resource] /
SALMASO, LUIGI.author.
Brombin, Chiara.author.
Statistical shape analysis is a geometrical analysis from a set of shapes in which statistics are measured to describe geometrical properties from similar shapes or different groups, for instance, the difference between male and female Gorilla skull shapes, normal and pathological bone shapes, etc. Some of the important aspects of shape analysis are to obtain a measure of distance between shapes, to estimate average shapes from a (possibly random) sample and to estimate shape variability in a sample[1]. One of the main methods used is principal component analysis. Specific applications of shape analysis may be found in archaeology, architecture, biology, geography, geology, agriculture, genetics, medical imaging, security applications such as face recognition, entertainment industry (movies, games), computer-aided design and manufacturing. This is a proposal for a new Brief on statistical shape analysis and the various new parametric and non-parametric methods utilized to facilitate shape analysis. .
Description based upon print version of record.
Preface; Contents; 1 A Brief Overview on Statistical Shape Analysis; 1.1 Some Historical Notes; 1.2 How to Describe Shapes; 1.2.1 Monk Seal Skulls Study; 1.2.2 Example of Principal Facial Landmarks; 1.3 Multivariate Morphometrics; References; 2 Theoretical Aspects on Permutation Tests and Shape Analysis; 2.1 Inference and Shape Analysis; 2.2 Technical Details on Tests Known in Shape Analysis Literature; 2.2.1 Hotelling's T2 and Goodall's F Tests; 2.2.2 Euclidean Distance Matrix Analysis Methods; 2.3 NPC Approach to Shape Analysis; 2.3.1 A Suitable Algorithm; 2.3.2 Including MA Procedure
2.3.3 Closed Testing Procedure in Shape Analysis2.4 Permutation Version of Hotelling's T2; References; 3 Evaluating Power Behavior of Nonparametric Combination Testing Methodology After Generalized Procrustes Analysis and Under Different Correlation Structures; 3.1 Generalized Procrustes Analysis and Power of NonParametric Combination methodology; 3.2 Introducing Correlation Between Landmarks; 3.3 Paired Data Problems: Study of Symmetric Structures; References; 4 Power Behavior of Permutation Tests with High Dimensional Data; 4.1 High Dimensional Data with Small Sample Sizes
4.2 Simulation Study and Results4.3 Final Remarks; References; 5 Finite-Sample Consistency of Combination-Based Tests in Shape Analysis; 5.1 How to Obtain a Tangent Space: A Brief Overview; 5.2 Main Theorems and General Characterization of Finite Sample Consistency; 5.3 A Toy Example; References; 6 Applications to Real Case Studies; 6.1 Biometric Morphing and Facial Expression of Emotions; 6.2 Some Remarks on Iterated Combination; 6.3 On Morphology of Aortic Valve; 6.3.1 Introduction; 6.3.2 Inferential Results; 6.4 Final Remarks; References
Includes bibliographical references.
Statistical shape analysis is a geometrical analysis from a set of shapes in which statistics are measured to describe geometrical properties from similar shapes or different groups, for instance, the difference between male and female Gorilla skull shapes, normal and pathological bone shapes, etc. Some of the important aspects of shape analysis are to obtain a measure of distance between shapes, to estimate average shapes from a (possibly random) sample and to estimate shape variability in a sample[1]. One of the main methods used is principal component analysis. Specific applications of shape analysis may be found in archaeology, architecture, biology, geography, geology, agriculture, genetics, medical imaging, security applications such as face recognition, entertainment industry (movies, games), computer-aided design and manufacturing. This is a proposal for a new Brief on statistical shape analysis and the various new parametric and non-parametric methods utilized to facilitate shape analysis. .
Statistics .
Statistics, general.
Statistical Theory and Methods.
Statistics for Life Sciences, Medicine, Health Sciences.