ML Anthology

An Integrated Approach to Feature Based Dynamic Vision

Ernst D. Dickmanns
CVPR 1988 pp. 820-825
doi:10.1109/CVPR.1988.196328 /cvpr/1988/dickmanns1988cvpr-integrated/

Abstract

A novel method for dynamic scene analysis by computer vision is described that combines 3-D shape models, dynamical models as known from modern control theory and the laws of perspective projection. To arrive at numerically efficient real-time algorithms, the recursive state estimation by Kalman filtering is adapted to a feature-based image sequence analysis scheme. The spatial and temporal constraint propagation using an integral spatiotemporal model yields image evaluation cycle times of about 0.1 s for simple but realistic tasks with microprocessors available today. Motion control in the dynamic range of humans is thereby possible. Applications discussed are: three-degree-of-freedom planar docking, road vehicle guidance at speeds up to 60 mph and six-degree-of-freedom landing approach of a business jet plane (hardware in the loop simulation).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

CVPR Semantic Scholar

Cite

Text

Dickmanns. "An Integrated Approach to Feature Based Dynamic Vision." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1988. doi:10.1109/CVPR.1988.196328

Markdown

[Dickmanns. "An Integrated Approach to Feature Based Dynamic Vision." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1988.](https://mlanthology.org/cvpr/1988/dickmanns1988cvpr-integrated/) doi:10.1109/CVPR.1988.196328

BibTeX

@inproceedings{dickmanns1988cvpr-integrated,
  title     = {{An Integrated Approach to Feature Based Dynamic Vision}},
  author    = {Dickmanns, Ernst D.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1988},
  pages     = {820-825},
  doi       = {10.1109/CVPR.1988.196328},
  url       = {https://mlanthology.org/cvpr/1988/dickmanns1988cvpr-integrated/}
}