An af®ne-invariant (AI) deformable contour model for object matching, called AI-EigenSnake (AI-ES), is proposed in the Bayesian framework. In AI-ES, the prior distribution of object shapes is estimated from the sample data. This distribution is then used to constrain the prototype contour, which is dynamically adjustable in the matching process. In this way, large shape deformations due to the ...

The ubiquity of the Laplace-Beltrami operator in shape analysis can be seen by observing the wide variety of applications where it has been found to be useful. Here we demonstrate a small subset of such uses with their latest developments including a scale invariant transform for general triangulated meshes, an effective and efficient method for denoising meshes using Beltrami flows via high di...

This paper concerns the abstraction of sets of functions for use in abstract interpretation. The paper gives an overview of existing methods, which are illustrated with applications to shape analysis, and formalizes a new family of relational abstract domains that allows sets of functions to be abstracted more precisely than with known approaches, while being still machine-representable.

This paper introduces a novel shape representation which performs shape analysis in a hierarchical fashion using Gaussian and Laplacian pyramids. A background on hierarchical shape analysis is given along with a detailed explanation of the hierarchical method, and results are shown on natural contours. A comparison is performed between the new method and our proposed approach using Point Distri...

Taking up and extending earlier suggestions, we show how twoand three-dimensional shapes of secondorder HBT correlations can be described in a multivariate Edgeworth expansion around Gaussian ellipsoids, with expansion coefficients, identified as the cumulants of pair momentum difference q, acting as shape parameters. Off-diagonal terms dominate both the character and magnitude of shapes. Cumul...

In this technical report we derive the analytic form of the Hessian matrix for shape matching energy. Shape matching (Fig. 1) is a useful technique for meshless deformation, which can be easily combined with multiple techniques in real-time dynamics (refer to [MHTG05, BMM15] for more details). Nevertheless, it has been rarely applied in scenarios where implicit (such as backward differentiation...

3-D anthropometry data obtained from 3-D scanning technology provide detailed information about the human shape. However, due to the limitations of the sensors, such as field of view, lighting condition, and object self-occlusion, the raw 3-D data are usually noisy and incomplete. Much processing is necessary before they can be used for statistical analysis. In this paper, we present geometry p...

We offer a blazingly brief review of evolution of shape analysis methods in medical imaging. As the representations and the statistical models grew more sophisticated, the problem of shape analysis has been gradually redefined to accept images rather than binary segmentations as a starting point. This transformation enabled shape analysis to take its rightful place in the arsenal of tools for e...

In this paper we study the problem of shape analysis and its application in locating facial feature points on frontal faces. We propose a Bayesian inference solution based on tangent shape approximation called Bayesian Tangent Shape Model (BTSM). Similarity transform coefficients and the shape parameters in BTSM are determined through MAP estimation. Tangent shape vector is treated as the hidde...

The aim of this paper is to adapt the Viability Theorem from differential inclusions (governing the evolution of vectors in a finite dimensional space) to so-called morphological inclusions (governing the evolution of nonempty compact subsets of the Euclidean space). In this morphological framework, the evolution of compact subsets of RN is described by means of flows along bounded Lipschitz ve...