Darshan Bryner , Naval Surface Warfare Center Panama City Division, Panama City and Florida State University, Tallahassee
Eric Klassen , Florida State University, Tallahassee
Huiling Le , University of Nottingham, Nottingham
Anuj Srivastava , Florida State University, Tallahassee
Current techniques for shape analysis tend to seek invariance to similarity transformations (rotation, translation and scale), but certain imaging situations require invariance to larger groups, such as affine or projective groups. Here we present a general Riemannian framework for shape analysis of planar objects where metrics and related quantities are invariant to affine and projective groups. Highlighting two possibilities for representing object boundaries -- ordered points (or landmarks) and parameterized curves -- we study different combinations of these representations (points and curves) and transformations (affine and projective). Specifically, we provide solutions to three out of four situations and develop algorithms for computing geodesics and intrinsic sample statistics, leading up to Gaussian-type statistical models, and classifying test shapes using such models learned from training data. In the case of parameterized curves, we also achieve the desired goal of invariance to re-parameterizations. The geodesics are constructed by particularizing the path-straightening algorithm to geometries of current manifolds and are used, in turn, to compute shape statistics and Gaussian-type shape models. We demonstrate these ideas using a number of examples from shape and activity recognition.
Shape, Space vehicles, Orbits, Standardization, Manifolds, Measurement, Computational modeling, Shape models, Affine invariance, Projective invariance, Riemannian methods, Elastic metric, Shape statistics, Karcher mean shapes
H. Le, A. Srivastava, E. Klassen and D. Bryner, "2D Affine and Projective Shape Analysis," in IEEE Transactions on Pattern Analysis & Machine Intelligence.