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 metric simplifies to the IL2 metric, the reparameterization group acts by isometries, and the space of unit length curves becomes the unit sphere. The shape space of closed curves is the quotient space of (a submanifold of) the unit sphere, modulo rotation, and reparameterization groups, and we find geodesics in that space using a path straightening approach. These geodesics and geodesic distances provide a framework for optimally matching, deforming, and comparing shapes. These ideas are demonstrated using: 1) shape analysis of cylindrical helices for studying ...
We describe two Riemannian frameworks for statistical shape analysis of parameterized surfaces. Thes...
Shape analysis of curves in Rn is an active research topic in computer vision. While shape itself is...
We propose a metric learning paradigm, Regression-based Elastic Metric Learning (REML), which optimi...
This paper illustrates and extends an efficient framework, called the square-root-elastic (SRE) fram...
A main goal in the field of statistical shape analysis is to define computable and informative metri...
We propose a novel representation of continuous, closed curves in Rn that is quite efficient for ana...
This paper illustrates and extends an efficient framework, called the square-root-elastic (SRE) fram...
Shape is an important physical property of natural and man-made 3D objects that characterizes their ...
In this paper, we introduce a similarity metric for curved shapes that can be described, distinctive...
In this paper, we introduce a similarity metric for curved shapes that can be described, distinctive...
Recent developments in elastic shape analysis (ESA) are motivated by the fact that it provides a com...
In this paper we define a new methodology for shape analysis of parameterized surfaces, where the ma...
International audienceWe present a Riemannian framework for geometric shape analysis of curves, func...
International audienceWe present a Riemannian framework for geometric shape analysis of curves, func...
We propose an efficient representation for studying shapes of closed curves in Rn. This paper combin...
We describe two Riemannian frameworks for statistical shape analysis of parameterized surfaces. Thes...
Shape analysis of curves in Rn is an active research topic in computer vision. While shape itself is...
We propose a metric learning paradigm, Regression-based Elastic Metric Learning (REML), which optimi...
This paper illustrates and extends an efficient framework, called the square-root-elastic (SRE) fram...
A main goal in the field of statistical shape analysis is to define computable and informative metri...
We propose a novel representation of continuous, closed curves in Rn that is quite efficient for ana...
This paper illustrates and extends an efficient framework, called the square-root-elastic (SRE) fram...
Shape is an important physical property of natural and man-made 3D objects that characterizes their ...
In this paper, we introduce a similarity metric for curved shapes that can be described, distinctive...
In this paper, we introduce a similarity metric for curved shapes that can be described, distinctive...
Recent developments in elastic shape analysis (ESA) are motivated by the fact that it provides a com...
In this paper we define a new methodology for shape analysis of parameterized surfaces, where the ma...
International audienceWe present a Riemannian framework for geometric shape analysis of curves, func...
International audienceWe present a Riemannian framework for geometric shape analysis of curves, func...
We propose an efficient representation for studying shapes of closed curves in Rn. This paper combin...
We describe two Riemannian frameworks for statistical shape analysis of parameterized surfaces. Thes...
Shape analysis of curves in Rn is an active research topic in computer vision. While shape itself is...
We propose a metric learning paradigm, Regression-based Elastic Metric Learning (REML), which optimi...