Titlebook: Riemannian Computing in Computer Vision; Pavan K. Turaga,Anuj Srivastava Book 2016 The Editor(s) (if applicable) and The Author(s), under

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Canopy

Canonical Correlation Analysis on SPD(,) Manifoldsand has found a multitude of applications in computer vision, medical imaging, and machine learning. The classical formulation assumes that the data live in a pair of . which makes its use in certain important scientific domains problematic. For instance, the set of symmetric positive definite matri

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Robust Estimation for Computer Vision Using Grassmann Manifolds studied for Euclidean spaces and their use has also been extended to Riemannian spaces. In this chapter, we present the necessary mathematical constructs for Grassmann manifolds, followed by two different algorithms that can perform robust estimation on them. In the first one, we describe a nonline

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Covariance Weighted Procrustes Analysisetely general covariance matrix, extending previous approaches based on factored covariance structures. Procrustes matching is used to compute the Riemannian metric in shape space and is used more widely for carrying out inference such as estimation of mean shape and covariance structure. Rather tha

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Elastic Shape Analysis of Functions, Curves and Trajectoriesnd trajectories can also have important geometric features, we use shape as an all-encompassing term for the descriptors of curves, scalar functions and trajectories. Our framework relies on functional representation and analysis of curves and scalar functions, by square-root velocity fields (SRVF)

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