Recognition and Semi-Differential Invariants

Abstract

Semidifferential invariants, combining coordinates in different points together with their derivatives, are used for the description of planar contours. Their use can be seen as a tradeoff between two extreme strategies currently used in shape recognition: (invariant) feature extraction methods, involving high-order derivatives, and invariant coordinate descriptions, leading to the correspondence problem of reference points. The method for the derivation of such invariants, based on Lie group theory and applicable to a wide spectrum of transformation groups, is described. As an example, invariant curve parameterizations are developed for affine and projective transformations. The usefulness of the approach is illustrated with two examples: (1) recognition of a test set of 12 planar objects viewed under conditions allowing affine approximations, and (2) the detection of symmetry in perspective projections of curves.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cite

Text

Van Gool et al. "Recognition and Semi-Differential Invariants." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1991. doi:10.1109/CVPR.1991.139735

Markdown

[Van Gool et al. "Recognition and Semi-Differential Invariants." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1991.](https://mlanthology.org/cvpr/1991/gool1991cvpr-recognition/) doi:10.1109/CVPR.1991.139735

BibTeX

@inproceedings{gool1991cvpr-recognition,
  title     = {{Recognition and Semi-Differential Invariants}},
  author    = {Van Gool, Luc and Kempenaers, P. and Oosterlinck, André},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1991},
  pages     = {454-460},
  doi       = {10.1109/CVPR.1991.139735},
  url       = {https://mlanthology.org/cvpr/1991/gool1991cvpr-recognition/}
}