This paper proposes a novel framework for multi-group shape analysis relying on a hierarchical graphical statistical model on shapes within a population.The framework represents individual shapes as point setsmodulo translation, rotation, and scale, following the notion in Kendall shape space.While individual shapes are derived from their group shape model, each group shape model is derived from a single population shape model. The hierarchical model follows the natural organization of population data and the top level in the hierarchy provides a common frame of reference for multigroup shape analysis, e.g. classification and hypothesis testing. Unlike typical shape-modeling approaches, the proposed model is a generative model that defines a joint distribution of object-boundary data and the shape-model variables. Furthermore, it naturally enforces optimal correspondences during the process of model fitting and thereby subsumes the so-called correspondence problem. The proposed inference scheme employs an expectation maximization (EM) algorithm that treats the individual and group shape variables as hidden random variables and integrates them out before estimating the parameters (population mean and variance and the group variances). The underpinning of the EM algorithm is the sampling of pointsets, in Kendall shape space, from their posterior distribution, for which we exploit a highly-efficient scheme based on Hamiltonian Monte Carlo simulation. Experiments in this paper use the fitted hierarchical model to perform (1) hypothesis testing for comparison between pairs of groups using permutation testing and (2) classification for image retrieval. The paper validates the proposed framework on simulated data and demonstrates results on real data.
 Preliminaries: Size Measures and Shape Coordinates. Preliminaries: Planar Procrustes Analysis. Shape Space and Distance. General Procrustes Methods. Shape Models for Two Dimensional Data. Tangent Space Inference. Size--and--Shape. Distributions for Higher Dimensions. Deformations and Describing Shape Change. Shape in Images. Additional Topics. References and Author Index. Index. One. Introduction: On the Absence of Geometry from Morphometrics.- First Part. The Measurement of Biological Shape.- Two. Shapes and Measures of Shape.- A. Properties of the Euclidean Plane and Euclidean Space.- B. Outlines and Homologous Landmarks.- C. Definitions of Shape, Shape Change, Shape Measurement.- D. Shapes as Data.- Three. Critique of an Applied Field: Conventional Cephalometrics.- A. Landmarks, Curvature, and Growth.- B. Registration.- Four. New Statistical Methods for Shape.- A. Analysis of the Tangent Angle Function.- 1. History.- 2. Sampling from the tangent angle function.- 3. Conic replacement curves and their estimation Fit of a conic to a point - Geometric interpretation - Estimator for a circle - Estimation for the general conic - Computation of the extremum -Linear constraints.- 4. Conic splining Joint conic fitting under constraint -An example - Application - Analysis of parameters.- B. Extension to Three Dimensions: A Sketch.- C. Skeletons Definition - A multivariate statistical method - Bibliographic note.- Second Part. The Measurement of Shape Change Using Biorthogonal Grids.- Five. The Study of Shape Transformation after D'Arcy Thompson.- A. The Original Method.- 1. Thompson's own work.- 2. Later examples.- 3. Difficulties.- B. Analysis of Growth Gradients.- C. Simulations.- D. Other Morphometric Schemes Vector displacements - Multivariate morphometrics.- Six. The Method of Biorthogonal Grids.- A. Representation of Affine Transformations.- B. General Lines of Growth and Biorthogonal Grids.- C. Summarizing the Grids.- Technical Note 1. Existence and Form of Biorthogonal Grids.- Technical Note 2. Interpolation from Landmark Locations and Arcs The measure of roughness - The vector space and its associated functions - Interpolation from boundary values - Interpolation from boundary values and interior points - Note on computation.- Technical Note 3. Construction of Integral Curves.- Technical Note 4. On Homologous Points.- Seven. Examples of Biorthogonal Analysis.- A. Comparison of Square and Biorthogonal Grids: Thompson's Diodon Figure.- B. Phylogeny and Ontogeny of Primate Crania.- 1. Functional craniology and craniometrics.- 2. Data for this exercise.- 3. Two types of transformations.- 4. Comparative ontogeny of the apes and man.- Eight. Future Directions for Transformation Analysis.- A. Statistical Methods The symmetric tensor field - Concordance -Linear methods.- B. Computation Other kinds of information about homology -Three dimensions.- C. Likely Applications Computed tomography - Orthodontics - Developmental biology.- Nine. Envoi.- Literature Cited. The shape-space l. k m whose points a represent the shapes of not totally degenerate c-ads in IR m is introduced as a quotient space carrying the quotient metric. When m = 1, we find that Y = S k 2 ; when m ^ 3, the shape-space contains singularities. This paper deals mainly with the case m = 2, when the shape-space I* ca n be identified with a version of CP* 2 . Of special importance are the shape-measures induced on CP k 2 by any assigned diffuse law of distribution for the k vertices. We determine several such shape-measures, we resolve some of the technical problems associated with the graphic presentation and statistical analysis of empirical shape distributions, and among applications we discuss the relevance of these ideas to testing for the presence of non-accidental multiple alignments in collections of (i) neolithic stone monuments and (ii) quasars. Finally the recently introduced Ambartzumian density is examined from the present point of view, its norming constant is found, and its connexion with random Crofton polygons is established.