Output-sensitive volume tracking

Lian Jiang; XiaoLin Li

doi:10.1117/12.658175

16 January 2006 Output-sensitive volume tracking

Lian Jiang, XiaoLin Li

Proceedings Volume 6060, Visualization and Data Analysis 2006; 606012 (2006) https://doi.org/10.1117/12.658175
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

Feature tracking is a useful technique for studying the evolution of phenomena (or features) in time-varying scientific datasets. Time-varying datasets can be massive and are constantly becoming larger as more powerful machines are being used for scientific computations. To interactively explore such datasets feature tracking must be done efficiently. For massive datasets, which do not fit into memory, tracking should be done out-of-core. In this paper, we propose an "output-sensitive" feature tracking, which uses the pre-computed metadata to (1) enable out-of-core processing structured datasets, (2) expedite the feature tracking processing, and (3) make the feature tracking less threshold sensitive. With the assistance of the pre-computed metadata, the complexity of the feature extraction is improved from O(mlgm) to O(n), where m is the number of cells in a timestep and n is the number of cells in just the extracted features. Furthermore, the feature tracking's complexity is improved from O(nlgn) to O(nlgk), where k is the number of cells in a feature group. The metadata computation and feature tracking can easily be adapted to the out-of-core paradigm. The effectiveness and efficiency of this algorithm is demonstrated using experiments.

Citation Download Citation

Lian Jiang and XiaoLin Li "Output-sensitive volume tracking", Proc. SPIE 6060, Visualization and Data Analysis 2006, 606012 (16 January 2006); https://doi.org/10.1117/12.658175

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KEYWORDS

Feature extraction

Detection and tracking algorithms

Printed circuit board testing

Binary data

Visualization

Computer simulations

Optical tracking

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