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In this article, we present trajectory representation algorithms for tangible features found in temporally varying scientific datasets. Rather than modeling the features as points, we take attributes like shape and extent of the feature into account. Our contention is that these attributes play an important role in understanding the temporal evolution and interactions among features. The proposed...
Spatio-temporal relationships among features extracted from temporally-varying scientific datasets can provide useful information about the evolution of an individual feature and its interactions with other features. However, extracting such useful relationships without user guidance is cumbersome and often an error prone process. In this paper, we present a visual analysis system that interactively...
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