Abstract Applications in computer vision involve statistically analyzing an important class of constrained, nonnegative functions, including probability density functions (in texture analysis), dynamic time-warping functions (in activity analysis), and re-parametrization or non-rigid registration functions (in shape analysis of curves). For this one needs to impose a Riemannian
In computer vision, a wide range of problems involving probability density functions, dynamic time-warping and non-rigid registration of curves can be cast in a form where the data is embedded on a hypersphere REF .
Riemannian Analysis of Probability Density Functions with Applications in Vision
2007 IEEE Conference on Computer Vision and Pattern Recognition
2007 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science