Function and shape data analysis are old topics in statistics, studied off and on over the last
several decades. However, the early years of the new millennium saw a renewed focus and …

A thoroughly revised and updated edition of this introduction to modern statistical methods
for shape analysis Shape analysis is an important tool in the many disciplines where objects …

This paper introduces a square-root velocity (SRV) representation for analyzing shapes of
curves in euclidean spaces under an elastic metric. In this SRV representation, the elastic …

Implicit representations can provide simple descriptions of relatively complex shapes and
can in many cases be a good choice when designing stable shape processing algorithms …

Recognizing human actions in 3-D video sequences is an important open problem that is
currently at the heart of many research domains including surveillance, natural interfaces …

Constructing generative models for functional observations is an important task in statistical
functional analysis. In general, functional data contains both phase (or x or horizontal) and …

We propose a novel representation of continuous, closed curves in R n that is quite efficient
for analyzing their shapes. We combine the strengths of two important ideas-elastic shape …

Computing, visualizing and interpreting statistics on shapes like curves or surfaces is a real
challenge with many applications ranging from medical image analysis to computer …

Motivated by the problems of analyzing protein backbones, diffusion tensor magnetic
resonance imaging (DT-MRI) fiber tracts in the human brain, and other problems involving …

In this paper, we consider the family of total Bregman divergences (tBDs) as an efficient and
robust “distance” measure to quantify the dissimilarity between shapes. We use the tBD …