• TUCS Publication Series
• How to Publish in TUCS Series
• Publication Search
• Publication Graph
• Publication Input
• Publication Input Guide
• JuFo Browser

Ridge-Based Method for Finding Curvilinear Structures from Noisy Data

Seppo Pulkkinen, Ridge-Based Method for Finding Curvilinear Structures from Noisy Data. Computational Statistics and Data Analysis 82, 89–109, 2015.

http://dx.doi.org/10.1016/j.csda.2014.08.007

Abstract:

Extraction of curvilinear structures from noisy data is an essential task in many application fields such as data analysis, pattern recognition and machine vision. The proposed approach assumes a random process in which the samples are obtained from a generative model. The model specifies a set of generating functions describing curvilinear structures as well as sampling noise and background clutter. It is shown that ridge curves of the marginal density induced by the model can be used to estimate the generating functions. Given a Gaussian kernel density estimate for the marginal density, ridge curves of the density estimate are parametrized as the solution to a differential equation. Finally, a predictor-corrector algorithm for tracing the ridge curve set of such a density estimate is developed. Efficiency and robustness of the algorithm are demonstrated by numerical experiments on synthetic datasets as well as observational datasets from seismology and cosmology.

BibTeX entry:

@ARTICLE{jPulkkinen_Seppo15a,
  title = {Ridge-Based Method for Finding Curvilinear Structures from Noisy Data},
  author = {Pulkkinen, Seppo},
  journal = {Computational Statistics and Data Analysis},
  volume = {82},
  pages = {89–109},
  year = {2015},
  keywords = {principal curve, filament, generative model, ridge curve, density estimation, predictor–corrector method},
  ISSN = {0167-9473},
}

Belongs to TUCS Research Unit(s): Turku Optimization Group (TOpGroup)

Publication Forum rating of this publication: level 2

Edit publication