ML Anthology

Error of Fit Measures for Recovering Parametric Solids

Ari D. Gross, Terrance E. Boult
ICCV 1988 pp. 690-694
doi:10.1109/CCV.1988.590052 /iccv/1988/gross1988iccv-error/

Abstract

Parametric models of objects are becoming increasingly more impor- tant in computer vision. In the past few years, a number of researchers have investigated the recovery of a class of parametric models by the minimization of an error of fit measure. The measures used have typ- ically been chosen in an ad hoc fashion. This paper looks at how these measures affect the performance of a recovery system. This research can be divided into two parts. The first studies the biases of the po- tential error-of-fit measures with respect to the parameters recovered and examines the cross-sectional shape of their respective error of fit surfaces. This study is done in simulation by holding all but one pa- rameter constant. The second part of the research compares two of the better error of fit measures by using them in a recovery system. Both the number of iterations and the quality of the reconstruction are considered.

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Cite

Text

Gross and Boult. "Error of Fit Measures for Recovering Parametric Solids." IEEE/CVF International Conference on Computer Vision, 1988. doi:10.1109/CCV.1988.590052

Markdown

[Gross and Boult. "Error of Fit Measures for Recovering Parametric Solids." IEEE/CVF International Conference on Computer Vision, 1988.](https://mlanthology.org/iccv/1988/gross1988iccv-error/) doi:10.1109/CCV.1988.590052

BibTeX

@inproceedings{gross1988iccv-error,
  title     = {{Error of Fit Measures for Recovering Parametric Solids}},
  author    = {Gross, Ari D. and Boult, Terrance E.},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {1988},
  pages     = {690-694},
  doi       = {10.1109/CCV.1988.590052},
  url       = {https://mlanthology.org/iccv/1988/gross1988iccv-error/}
}