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A nonparametric Riemannian framework on tensor field with application to foreground segmentation

https://doi.org/10.1109/iccv.2011.6126218

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Tensor Processing for Texture and Colour Segmentation

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Introduction to Smooth Manifolds

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Information and the Accuracy Attainable in the Estimation of Statistical Parameters

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Making Background Subtraction Robust to Sudden Illumination Changes

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Riemannian Geometry

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Manifolds all of whose Geodesics are Closed

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Semi-Riemannian Manifolds

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M�thodes de calcul diff�rentiel absolu et leurs applications

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Nozione di parallelismo in una varietà qualunque e conseguente specificazione geometrica della curvatura riemanniana

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Generalized Stauffer–Grimson background subtraction for dynamic scenes

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Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements

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Statistics on the Manifold of Multivariate Normal Distributions: Theory and Application to Diffusion Tensor MRI Processing

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A Riemannian Framework for Tensor Computing

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Kernel density estimation on Riemannian manifolds

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An Introduction to Differentiable Manifolds and Riemannian Geometry

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Riemannian Geometry

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Detection and segmentation of moving objects in highly dynamic scenes

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A Graph Cut Approach to Image Segmentation in Tensor Space

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Multi-Layer Background Subtraction Based on Color and Texture

inferredcites →https://doi.org/10.1109/cvpr.2008.4587576openalex-snapshot

Background subtraction in highly dynamic scenes

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Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes

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Foreground segmentation of live videos using locally competing 1SVMs

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Background modeling and subtraction of dynamic scenes

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Background Subtraction for Freely Moving Cameras

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Realtime background subtraction from dynamic scenes

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Dynamic background modeling and subtraction using spatio-temporal local binary patterns

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Recognition of local symmetries in gray value images by harmonic functions

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Detection and location of people in video images using adaptive fusion of color and edge information

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Foreground Segmentation via Background Modeling on Riemannian Manifolds

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DTI segmentation by statistical surface evolution

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Spatiotemporal Saliency in Dynamic Scenes

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STATISTICAL BACKGROUND MODELING FOR FOREGROUND DETECTION: A SURVEY

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DYNAMIC BACKGROUND SUBTRACTION BASED ON LOCAL DEPENDENCY HISTOGRAM

inferredcites →https://doi.org/10.1155/2010/782101openalex-snapshot

Local Histogram of Figure/Ground Segmentations for Dynamic Background Subtraction

inferredcites →https://doi.org/10.1155/asp.2005.2281openalex-snapshot

Spatio-temporal Background Models for Outdoor Surveillance

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Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art

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Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey

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Subspace Learning for Background Modeling: A Survey

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Image Segmentation in Video Sequences: A Probabilistic Approach

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A Combined Corner and Edge Detector

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Background Modelling on Tensor Field for Foreground Segmentation

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Spatiotemporal Algorithm For Joint Video Segmentation And Foreground Detection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Riemannian Manifolds For Brain Extraction On Multi-Modal Resonance Magnetic Images

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Semi-intrinsic Mean Shift on Riemannian Manifolds

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Kernel-Based Subspace Learning on Riemannian Manifolds for Visual Recognition

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3D Human Pose Tracking Priors using Geodesic Mixture Models

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Riemannian Gaussian distributions, random matrix ensembles and diffusion kernels

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A nonparametric Riemannian framework on tensor field with application to foreground segmentation

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Low Rank Representation on SPD matrices with Log-Euclidean metric

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Log‐Euclidean bag of words for human action recognition

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Time Series Data Classification on Grassmann Manifold

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Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem

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Beyond the shortest path: Unsupervised domain adaptation by Sampling Subspaces along the Spline Flow

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Log-Euclidean Kernels for Sparse Representation and Dictionary Learning

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A Bayesian Approach to Camouflaged Moving Object Detection

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Stacked Multilayer Self-Organizing Map for Background Modeling

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Gaussian Distributions on Riemannian Symmetric Spaces: Statistical Learning With Structured Covariance Matrices

inferred← citeshttps://doi.org/10.1109/tits.2012.2212432openalex-snapshot

Spatio-Temporal Traffic Scene Modeling for Object Motion Detection

inferred← citeshttps://doi.org/10.1109/tpami.2014.2361137openalex-snapshot

Free-Form Region Description with Second-Order Pooling

inferred← citeshttps://doi.org/10.1109/tpami.2015.2414422openalex-snapshot

Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels

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Hilbert–Schmidt Independence Criterion Regularization Kernel Framework on Symmetric Positive Definite Manifolds

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Inference for partially observed Riemannian Ornstein–Uhlenbeck diffusions of covariance matrices

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Gaussian distributions on Riemannian symmetric spaces, random matrices, and planar Feynman diagrams

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