Singular features in sea surface temperature data

Q. Yang, B. Parvin, A. Mariano

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

We propose to detect singular features in order to generate an intelligent summary of high resolution spatiotemporal data that are obtained from satellite-based observations of the ocean. Toward this objective, we extend the Horn-Schunck model of flow field computation to incorporate incompressibility for tracking fluid motion. This is expressed as a zero-divergence constraint in the variational problem and an efficient multigrid implementation of it is introduced. Additionally, we show an effective localization of event features, such as vortices and saddle points, in the velocity field that can be used for subsequent abstraction, query and statistical analysis.

Original languageEnglish (US)
Pages (from-to)516-520
Number of pages5
JournalProceedings - International Conference on Pattern Recognition
Volume15
Issue number1
StatePublished - Dec 1 2000

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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