Abstract
This chapter reports on multigrid convergence based evaluation of surface approximations, and this approach is suggested to be one option for modelbased evaluations of computer vision algorithms in general. This criterion is in common use in numerical mathematics. In general, algorithms may be judged according to criteria, such as methodological complexity of underlying theory, expected time for implementation, or run-time behaviour and storage requirements of the implemented algorithm. Accuracy is an important criterion as well, and this can be modeled as convergence towards the true value for grid based calculations.
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Klette, R., Wu, F., Zhou, SZ. (2000). Multigrid Convergence Based Evaluation of Surface Approximations. In: Klette, R., Stiehl, H.S., Viergever, M.A., Vincken, K.L. (eds) Performance Characterization in Computer Vision. Computational Imaging and Vision, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9538-4_19
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DOI: https://doi.org/10.1007/978-94-015-9538-4_19
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