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2008
IEEE

Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision

11 years 12 days ago
Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision
Many applications in computer vision and pattern recognition involve drawing inferences on certain manifoldvalued parameters. In order to develop accurate inference algorithms on these manifolds we need to a) understand the geometric structure of these manifolds b) derive appropriate distance measures and c) develop probability distribution functions (pdf) and estimation techniques that are consistent with the geometric structure of these manifolds. In this paper, we consider two related manifolds - the Stiefel manifold and the Grassmann manifold, which arise naturally in several vision applications such as spatio-temporal modeling, affine invariant shape analysis, image matching and learning theory. We show how accurate statistical characterization that reflects the geometry of these manifolds allows us to design efficient algorithms that compare favorably to the state of the art in these very different applications. In particular, we describe appropriate distance measures and parame...
Pavan K. Turaga, Ashok Veeraraghavan, Rama Chellap
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Pavan K. Turaga, Ashok Veeraraghavan, Rama Chellappa
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