Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the tensor of curvature of a surface from a polyhedral approximation
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
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Shape remains a major descriptive and discriminant feature of objects, especially in clinical settings where it forms an integral part (for example) of radiological morphometry as well as anthropometry and biometrics. Most successful descriptions of shape are functional rather than descriptive, and it is often the goal that any such functional description of shape be unique (but generalizable, see below) as well as invariant under some (or all) rigid motions of the base object. When these goals are combined with a desire to keep the descriptor reasonably computationally efficient then often a compromise had to be made in which one or other of the goals is relaxed. In this paper we describe a vectorial formulation of an invariant shape descriptor which is reasonably computationally efficient but for which we cannot guarantee absolute uniqueness. Our setting is a wide class of three-dimensional (polyhedral) objects drawn from such datasets as the Visible Human datasets and the CAESAR/CARDLab[1] anthropometric datasets. In this short paper we describe the formal definition of a set of vectorial spherical shape descriptors, and give preliminary indications of their role in shape description in anthropometry.