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Proceedings Paper

Feature extraction and tracking in oceanographic visualization
Author(s): Zhifan Zhu; Robert J. Moorhead; Harsh Anand; Lakhamraju R. Raju
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Paper Abstract

The Ocean contains complex physical phenomena that evolve over both space and time and exhibit many dynamic mesoscale features, like eddies and fronts. Studying the evolution, deformation, and interaction of these dynamic features and accurately modeling them is the essence of research in ocean circulation. Scientific visualization provides an effective means for scientists to study the evolution and interaction of oceanographic features in large time-varying, multiparameter data sets. One of the goals of oceanographic visualization is to build and validate the mathematical models. This generally necessitates an accurate tracking and therefore numerical description of those ocean features over space and time. Many visualization techniques have been found effective in providing insight. However, a desirable capability for visualization of mesoscale features is automatically recognizing and tracking underlying features in oceanographic data sets. Important features that may not be anticipated in large data sets should be detected automatically during the visualization process. The existence and therefore the locations of features are often unknown even to a knowledgeable user at the beginning. Without an automatic mechanisms, intensive and time-consuming searches must be performed, otherwise the features may not be revealed. In order to best characterize the features, locally optimized data classification must be achieved at each time step, each depth level and for each parameter. This is a difficult, if not impossible, task with traditional data classification methods. In this paper, we present the work we have done in addressing the feature extraction problem in four dimensional oceanographic visualization. We developed feature tracking algorithms that exploited the features' temporal and spatial correlations and applied them to tracking eddies over space and time.

Paper Details

Date Published: 4 April 1994
PDF: 9 pages
Proc. SPIE 2178, Visual Data Exploration and Analysis, (4 April 1994); doi: 10.1117/12.172074
Show Author Affiliations
Zhifan Zhu, Mississippi State Univ./NSF Engineering Research Ctr. for Computational Field Simulation (United States)
Robert J. Moorhead, Mississippi State Univ./NSF Engineering Research Ctr. for Computational Field Simulation (United States)
Harsh Anand, Mississippi State Univ. Ctr. for Air-Sea Technology (United States)
Lakhamraju R. Raju, Mississippi State Univ. Ctr. for Air-Sea Technology (United States)


Published in SPIE Proceedings Vol. 2178:
Visual Data Exploration and Analysis
Robert J. Moorhead; Deborah E. Silver; Samuel P. Uselton, Editor(s)

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