Elsevier

Image and Vision Computing

Volume 11, Issue 7, September 1993, Pages 440-446
Image and Vision Computing

Using line correspondences in invariant signatures for curve recognition

https://doi.org/10.1016/0262-8856(93)90047-KGet rights and content

Abstract

Plane curves and space curves distorted by an affine or projective transformation may be recognized if Invariant descriptions of them are available. Recent research in this area has shown that it is possible to identify transformed curves through the use of various combinations of differential invariants and point correspondences. Purely differential invariants usually require very high order derivatives of the space curves, though taking advantage of point correspondences sharply reduces the order of derivatives necessary. In cases where point correspondences are not available but line correspondences are, it is still possible to construct invariant signatures of the curves without increasing the order of derivatives necessary. Using just first order derivatives, invariant signature functions can be established for plane curves using one line correspondence for curves subjected to affine transformations and using two line correspondences for curves subjected to projective transformations. Still with only first order derivatives, invariant signatures can be found for space curves using two line correspondences for curves subjected to affine transformations, and using three line correspondences for curves subjected to projective transformations. In each of the four cases, these invariant signatures are graphs of one invariant quantity versus another. Determining the equivalence of objects then requires identification of a pair of two-dimensional graphs. Planar objects and surfaces In space may be recognized by matching their boundaries using these variants. Furthermore a group of partially occluded curves may be resolved Into Its individual components.

References (13)

  • E.B. Barrett et al.

    Contributions to the theory of projective invariants for curves in two and three dimensions

  • Bruckstein, A M, Holt, R J, Netravali, A N and Richardson, T J 'Invariant signatures for planar shape recognition under...
  • A.M. Bruckstein et al.

    On differential invariants of planar curves and the recognition of partially occluded planar shapes

  • R.J. Holt et al.

    Invariant signatures for space curve recognition

    AT&T Technical Memorandum 11256-920207.03TM

    (1992)
  • P Kempenaers et al.

    Shape recognition under affine distortions

  • L Van Gool et al.

    Recognition and semi-differential invariants

There are more references available in the full text version of this article.

Cited by (8)

  • Image Analysis and Computer Vision: 1993

    1994, Computer Vision and Image Understanding
  • Deep Reinforcement Learning for Object Segmentation in Video Sequences

    2017, Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016
  • Reinforced medical image segmentation

    2009, Computational Intelligence in Medical Imaging: Techniques and Applications
  • Opposite actions in reinforced image segmentation

    2008, Studies in Computational Intelligence
View all citing articles on Scopus
View full text