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Riemannian Computing in Computer Vision

1st ed. 2016, 2015

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Compressive Acquisition of Linear Dynamical Systems

SIAM journal on imaging sciences, 2013-01, Vol.6 (4), p.2109-2133

2013

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Welcome to Riemannian Computing in Computer Vision

Riemannian Computing in Computer Vision, 2015, p.1-18

2015

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Compressive Acquisition of Dynamic Scenes

Computer Vision – ECCV 2010, p.129-142

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From Videos to Verbs: Mining Videos for Activities using a Cascade of Dynamical Systems

2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007, p.1-8

2007

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Why Use Sobolev Metrics on the Space of Curves

Riemannian Computing in Computer Vision, p.233-255

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Statistical and geometric modeling of spatio-temporal patterns for video understanding

Turaga, Pavan K

2009

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Kernels on Riemannian Manifolds

Riemannian Computing in Computer Vision, p.45-67

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Elastic Shape Analysis of Functions, Curves and Trajectories

Riemannian Computing in Computer Vision, 2015, p.211-231

2015

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Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds

Riemannian Computing in Computer Vision, p.101-121

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Canonical Correlation Analysis on SPD(n) Manifolds

Riemannian Computing in Computer Vision, p.69-100

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Designing a Boosted Classifier on Riemannian Manifolds

Riemannian Computing in Computer Vision, p.281-301

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Elastic Shape Analysis of Surfaces and Images

Riemannian Computing in Computer Vision, p.257-277

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Covariance Weighted Procrustes Analysis

Riemannian Computing in Computer Vision, p.189-209

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Motion Averaging in 3D Reconstruction Problems

Riemannian Computing in Computer Vision, p.145-164

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Recursive Computation of the Fréchet Mean on Non-positively Curved Riemannian Manifolds with Applications

Riemannian Computing in Computer Vision, p.21-43

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Domain Adaptation Using the Grassmann Manifold

Riemannian Computing in Computer Vision, p.325-343

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Robust Estimation for Computer Vision Using Grassmann Manifolds

Riemannian Computing in Computer Vision, p.125-144

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Lie-Theoretic Multi-Robot Localization

Riemannian Computing in Computer Vision, p.165-186

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Coordinate Coding on the Riemannian Manifold of Symmetric Positive-Definite Matrices for Image Classification

Riemannian Computing in Computer Vision, p.345-361

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A General Least Squares Regression Framework on Matrix Manifolds for Computer Vision

Riemannian Computing in Computer Vision, p.303-323

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Summarization and Search Over Geometric Spaces

Riemannian Computing in Computer Vision, p.363-387

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Part III Shapes, Surfaces, and Trajectories

Riemannian Computing in Computer Vision, 2015

2015

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Part II Color, Motion, and Stereo

Riemannian Computing in Computer Vision, 2015

2015

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Intro

Riemannian Computing in Computer Vision, 2015

2015

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Riemannian Computing in Computer Vision

Compressive Acquisition of Linear Dynamical Systems

Welcome to Riemannian Computing in Computer Vision

Compressive Acquisition of Dynamic Scenes

From Videos to Verbs: Mining Videos for Activities using a Cascade of Dynamical Systems

Why Use Sobolev Metrics on the Space of Curves

Statistical and geometric modeling of spatio-temporal patterns for video understanding

Kernels on Riemannian Manifolds

Elastic Shape Analysis of Functions, Curves and Trajectories

Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds

Canonical Correlation Analysis on SPD(n) Manifolds

Designing a Boosted Classifier on Riemannian Manifolds

Elastic Shape Analysis of Surfaces and Images

Covariance Weighted Procrustes Analysis

Motion Averaging in 3D Reconstruction Problems

Recursive Computation of the Fréchet Mean on Non-positively Curved Riemannian Manifolds with Applications

Domain Adaptation Using the Grassmann Manifold

Robust Estimation for Computer Vision Using Grassmann Manifolds

Lie-Theoretic Multi-Robot Localization

Coordinate Coding on the Riemannian Manifold of Symmetric Positive-Definite Matrices for Image Classification

A General Least Squares Regression Framework on Matrix Manifolds for Computer Vision

Summarization and Search Over Geometric Spaces

Part III Shapes, Surfaces, and Trajectories

Part II Color, Motion, and Stereo

Intro

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