Feature extraction and smoothing of a noisy contour of a particle using a bayesian model

Toshiyuki Nonaka, Mutsumi Suzuki

Research output: Contribution to journalArticlepeer-review

Abstract

A method of feature extraction and smoothing of a noisy contour of a particle is developed using a Bayesian model. The time series representing boundary points of a contour is able to be decomposed into trend, periodical, autoregressive and observation error components by the fixed-interval smoother algorithm with the Kalman filter. The performance of this smoothing algorithm is illusrated with some examples.

Original languageEnglish
Pages (from-to)558-559
Number of pages2
Journalkagaku kogaku ronbunshu
Volume22
Issue number3
DOIs
Publication statusPublished - 1996

Keywords

  • Bayesian Statistics
  • Image Processing
  • Process System
  • Shape Analysis
  • Smoothing
  • Time series Analysis

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)

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