Detail publikace

Augmented Postprocessing of the FTLS Vectorization Algorithm

JELÍNEK, A. ŽALUD, L.

Originální název

Augmented Postprocessing of the FTLS Vectorization Algorithm

Anglický název

Augmented Postprocessing of the FTLS Vectorization Algorithm

Jazyk

en

Originální abstrakt

Vectorization is a widely used technique in many areas, mainly in robotics and image processing. Applications in these domains frequently require both speed (for real-time operation) and accuracy (for maximal information gain). This paper proposes an optimization for the high speed vectorization methods, which leads to nearly optimal results. The FTLS algorithm uses the total least squares method for fitting the lines into the point cloud and the presented augmentation for the refinement of the results, is based on a modified Nelder-Mead method. As shown on several experiments, this approach leads to better utilization of the information contained in the point cloud. As a result, the quality of approximation grows steadily with the number of points being vectorized, which was not achieved before. Performance costs are still comparable to the original algorithm, so the real-time operation is not endangered.

Anglický abstrakt

Vectorization is a widely used technique in many areas, mainly in robotics and image processing. Applications in these domains frequently require both speed (for real-time operation) and accuracy (for maximal information gain). This paper proposes an optimization for the high speed vectorization methods, which leads to nearly optimal results. The FTLS algorithm uses the total least squares method for fitting the lines into the point cloud and the presented augmentation for the refinement of the results, is based on a modified Nelder-Mead method. As shown on several experiments, this approach leads to better utilization of the information contained in the point cloud. As a result, the quality of approximation grows steadily with the number of points being vectorized, which was not achieved before. Performance costs are still comparable to the original algorithm, so the real-time operation is not endangered.

Dokumenty

BibTex


@inproceedings{BUT127074,
  author="Aleš {Jelínek} and Luděk {Žalud}",
  title="Augmented Postprocessing of the FTLS Vectorization Algorithm",
  annote="Vectorization is a widely used technique in many areas,  mainly in robotics and image processing.   Applications in these domains frequently require both speed (for real-time operation) and accuracy (for maximal information gain). This paper proposes an optimization for the high speed vectorization methods, which leads to nearly optimal results. The FTLS algorithm uses the total least squares method for fitting the lines into the point cloud and the presented augmentation for the refinement of the results, is based on a modified Nelder-Mead method.  As shown on several experiments, this approach leads to better utilization of the information contained in the point cloud.  As a result,  the quality of approximation grows steadily with the number of points being vectorized, which was not achieved before. Performance costs are still comparable to the original algorithm, so the real-time operation is not endangered.",
  booktitle="Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016) - Volume 2",
  chapter="127074",
  doi="10.5220/0005962902160223",
  howpublished="print",
  year="2016",
  month="july",
  pages="216--223",
  type="conference paper"
}