Efficiently Using Invariant Theory for Model-Based Matching

Wayner, Peter C.

doi:10.1109/CVPR.1991.139738

Efficiently Using Invariant Theory for Model-Based Matching

Peter C. Wayner

CVPR 1991 pp. 473-478

doi:10.1109/CVPR.1991.139738 /cvpr/1991/wayner1991cvpr-efficiently/

Abstract

A method is presented for efficiently maintaining and searching a database of three-dimensional models so they can be reliably recognized from arbitrary two-dimensional projections in the presence of noise and occlusion. The core of the process is the topologically defined network of invariants which breaks three-dimensional models down into small, local groups of features and indexes these groups using functions that are invariant under translation, rotation, scaling, and orthographic projection. The network encodes the geometrical relationships between these groups so that grouping information can be used to increase the speed of matching.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

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Cite

Text

Wayner. "Efficiently Using Invariant Theory for Model-Based Matching." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1991. doi:10.1109/CVPR.1991.139738

Markdown

[Wayner. "Efficiently Using Invariant Theory for Model-Based Matching." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1991.](https://mlanthology.org/cvpr/1991/wayner1991cvpr-efficiently/) doi:10.1109/CVPR.1991.139738

BibTeX

@inproceedings{wayner1991cvpr-efficiently,
  title     = {{Efficiently Using Invariant Theory for Model-Based Matching}},
  author    = {Wayner, Peter C.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1991},
  pages     = {473-478},
  doi       = {10.1109/CVPR.1991.139738},
  url       = {https://mlanthology.org/cvpr/1991/wayner1991cvpr-efficiently/}
}