ML Anthology

The Optimal Partition of Moving Edge Segments

Haisong Gu, Minoru Asada, Yoshiaki Shirai
CVPR 1993 pp. 367-372
doi:10.1109/CVPR.1993.341104 /cvpr/1993/gu1993cvpr-optimal/

Abstract

A method to obtain the optimal motion description from two consecutive images including multiple moving parts is presented. It copes with segmentation and motion estimation problems. Segmentation is necessary for motion estimation of each part, and vice versa. The authors propose to use an information measure approach, based on comparisons between an individual (or pixel) and a class (or set of pixels). First, the motion of an edge segment is optimally modeled. Next, merging and splitting processes are iterated until the minimum description is obtained for the whole image. As a result, the image is segmented into several regions, each of which is represented by an edge segment list, and, at the same time, the maximum likelihood motion estimation is obtained for each region. Experiments performed on real images are shown.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Gu et al. "The Optimal Partition of Moving Edge Segments." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993. doi:10.1109/CVPR.1993.341104

Markdown

[Gu et al. "The Optimal Partition of Moving Edge Segments." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1993.](https://mlanthology.org/cvpr/1993/gu1993cvpr-optimal/) doi:10.1109/CVPR.1993.341104

BibTeX

@inproceedings{gu1993cvpr-optimal,
  title     = {{The Optimal Partition of Moving Edge Segments}},
  author    = {Gu, Haisong and Asada, Minoru and Shirai, Yoshiaki},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1993},
  pages     = {367-372},
  doi       = {10.1109/CVPR.1993.341104},
  url       = {https://mlanthology.org/cvpr/1993/gu1993cvpr-optimal/}
}