Abstract
The Morse-Smale complex is an effective topology-based representation for identifying, ordering, and selectively removing features in scalar-valued data. Several algorithms are known for its effective computation, however, common problems pose practical challenges for any feature-finding approach using the Morse-Smale complex. We identify these problems and present practical solutions: (1) we identify the cause of spurious critical points due to simulation of simplicity, and present a general technique for solving it; (2) we improve simplification performance by reordering critical point cancellation operations and introducing an efficient data structure for storing the arcs of the complex; (3) we present a practical approach for handling boundary conditions.
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Acknowledgements
Attila Gyulassy was supported by the Lawrence Scholar Program (LSP). In addition, this research was supported in part by the National Science Foundation, under grant CCF-0702817. We would like to thank the members of the Center for Applied Scientific Computing (CASC), at LLNL, and the members of the Visualization and Computer Graphics Research Group of the Institute for Data Analysis and Visualization (IDAV), at UC Davis. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
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Gyulassy, A., Bremer, PT., Hamann, B., Pascucci, V. (2011). Practical Considerations in Morse-Smale Complex Computation. In: Pascucci, V., Tricoche, X., Hagen, H., Tierny, J. (eds) Topological Methods in Data Analysis and Visualization. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15014-2_6
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DOI: https://doi.org/10.1007/978-3-642-15014-2_6
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