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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Elastic Shape Analysis of Surfaces and Imageson, deformation, averaging, statistical modeling, and random sampling of surface shapes. A crucial property of both of these frameworks is that they are invariant to reparameterizations of surfaces. Thus, they result in natural shape comparisons and statistics. The first method we describe is based

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Designing a Boosted Classifier on Riemannian Manifoldsescriptors lying on a Riemannian manifold. This chapter describes a boosted classification approach that incorporates the a priori knowledge of the geometry of the Riemannian space. The presented classifier incorporated into a rejection cascade and applied to single image human detection task. Resul

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Pelago

Domain Adaptation Using the Grassmann Manifoldact that given data may have variations that can be difficult to incorporate into well-known, classical methods. One of these sources of variation is that of differing data sources, often called domain adaptation. Many domain adaptation techniques use the notion of a shared representation to attempt

MAL

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Book 2016s. This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the m

Leaven

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Salivary-Gland