ML Anthology

Finding Geometric and Relational Structures in an Image

Radu Horaud, Francoise Veillon, Thomas Skordas
ECCV 1990 pp. 374-384
doi:10.1007/BFB0014886 /eccv/1990/horaud1990eccv-finding/

Abstract

We present a method for extracting geometric and relational structures from raw intensity data. On one hand, low-level image processing extracts isolated features. On the other hand, image interpretation uses sophisticated object descriptions in representation frameworks such as semantic networks. We suggest an intermediate-level description between low- and high-level vision. This description is produced by grouping image features into more and more abstract structures. First, we motivate our choice with respect to what should be represented and we stress the limitations inherent with the use of sensory data. Second, we describe our current implementation and illustrate it with various examples.

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Cite

Text

Horaud et al. "Finding Geometric and Relational Structures in an Image." European Conference on Computer Vision, 1990. doi:10.1007/BFB0014886

Markdown

[Horaud et al. "Finding Geometric and Relational Structures in an Image." European Conference on Computer Vision, 1990.](https://mlanthology.org/eccv/1990/horaud1990eccv-finding/) doi:10.1007/BFB0014886

BibTeX

@inproceedings{horaud1990eccv-finding,
  title     = {{Finding Geometric and Relational Structures in an Image}},
  author    = {Horaud, Radu and Veillon, Francoise and Skordas, Thomas},
  booktitle = {European Conference on Computer Vision},
  year      = {1990},
  pages     = {374-384},
  doi       = {10.1007/BFB0014886},
  url       = {https://mlanthology.org/eccv/1990/horaud1990eccv-finding/}
}