Ridge-based method for finding curvilinear structures from noisy data
type: Item
instance of: scholarly article
title: Ridge-based method for finding curvilinear structures from noisy data
author: Seppo Pulkkinen
published in: Computational Statistics and Data Analysis
publication date: 2018-11-23T00:00:00Z
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 62P35
zbMATH DE Number: 6984108
zbMATH Keywords: principal curve
zbMATH Keywords: filament
zbMATH Keywords: generative model
zbMATH Keywords: ridge curve
zbMATH Keywords: density estimation
zbMATH Keywords: predictor-corrector method
describes a project that uses: KernSmooth
describes a project that uses: pyuvdata
MaRDI profile type: Publication
full work available at URL: j.csda.2014.08.007
OpenAlex ID: W2057391934
cites work: Multivariate plug-in bandwidth selection with unconstrained pilot bandwidth matrices
cites work: Asymptotics for general multivariate kernel density derivative estimators
cites work: Generic Structure of Two-Dimensional Images Under Gaussian Blurring
cites work: Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
cites work: Another look at principal curves and surfaces
cites work: Principal curves of oriented points: theoretical and computational improvements
cites work: Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation
cites work: Ridges in image and data analysis
cites work: On the path density of a gradient field
cites work: The Geometry of Nonparametric Filament Estimation
cites work: The Fast Gauss Transform
cites work: Principal Curves
cites work: Nonparametric density estimation and clustering in astronomical sky surveys
cites work: An algorithm for automatic curve detection
cites work: Q5396639
cites work: A continuation approach to mode-finding of multivariate Gaussian mixtures and kernel density estimates
cites work: A generative model and a generalized trust region Newton method for noise reduction
cites work: Q4863755
cites work: Detection, classification and estimation of individual shapes in 2D and 3D point clouds
cites work: Q4865051
DOI: 10.1016/J.CSDA.2014.08.007
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description: scientific article; zbMATH DE number 6984108