Abstract
Visualization of vector fields plays an important role in many applications. Vector fields can be described by differential equations. For classification null points, i.e. points where derivation is zero, are used. However, if vector field data are given in a discrete form, e.g. by data obtained by simulation or a measurement, finding of critical points is difficult due to huge amount of data to be processed and differential form usually used. This contribution describes a new approach for vector field null points detection and evaluation, which enables data compression and easier fundamental behavior visualization. The approach is based on implicit form representation of vector fields.
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Acknowledgment
The author would like to thank to colleagues at the University of West Bohemia and to anonymous reviewers for their comments, which helped to improve the manuscript significantly. Special thanks also belong to Pavel Šnejdar for MATLAB additional programming and images generation.
Research was supported by the Czech Science Foundation, No. GA 17–05534S and partially by SGS 2016-013.
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Skala, V., Smolik, M. (2019). A New Approach to Vector Field Interpolation, Classification and Robust Critical Points Detection Using Radial Basis Functions. In: Silhavy, R. (eds) Cybernetics and Algorithms in Intelligent Systems . CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 765. Springer, Cham. https://doi.org/10.1007/978-3-319-91192-2_12
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DOI: https://doi.org/10.1007/978-3-319-91192-2_12
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