Mahalanobis-based Adaptive Nonlinear Dimension Reduction

in 20th International Conference on Pattern Recognition (2010)

Meaningful 3D shape partitioning using Morse functions

in 2009 16th IEEE International Conference on Image Processing (2009)

Geometric modeling of rigid and non-rigid 3D shapes using the global geodesic function

in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. (2008)

Statistical Analysis of the Global Geodesic Function for 3D Object Classification

in IEEE International Conference on Acoustics, Speech and Signal Processing, 2007. ICASSP 2007 (2007)

This paper presents a novel classification strategy for 3D objects. Our technique is based on using a global geodesic function to intrinsically describe the surface of an object. The choice of the global ... [more ▼]

This paper presents a novel classification strategy for 3D objects. Our technique is based on using a global geodesic function to intrinsically describe the surface of an object. The choice of the global geodesic function ensures the invariance of the classification procedure to scaling and all isometric transformations. Using the Jensen-Shannon divergence, feature parameters are extracted from the probability distribution functions of the global geodesic function for each one of the classes. These parameters are used in the decision of a class membership of an object. This approach demonstrates low computational cost, efficiency, and robustness to resolution over many different data sets. [less ▲]