Representation and self-similarity of shapes

Tyng Luh Liu, Davi Geiger, Robert V. Kohn

Research output: Contribution to conferencePaperpeer-review


Representing shapes is a significant problem for vision systems that must recognize or classify objects. We derive a representation for a given shape by investigating its self-similarities, and constructing its shape axis(SA) and shape axis tree (SA-tree). We start with a shape, its boundary contour, and two different parameterizations for the contour. To measure its self-similarity we consider matching pairs of points (and their tangents) along the boundary contour, i.e., matching the two parameterizations. The matching, or self-similarity criteria may vary, e.g., co-circularity, parallelism, distance, region homogeneity. The loci of middle points of the pairing contour points are the shape axis and they can be grouped into a unique tree graph, the SA-tree. The shape axis for the co-circularity criteria is compared to the symmetry axis. An interpretation in terms of object parts is also presented.

Original languageEnglish (US)
Number of pages7
StatePublished - 1998
EventProceedings of the 1998 IEEE 6th International Conference on Computer Vision - Bombay, India
Duration: Jan 4 1998Jan 7 1998


OtherProceedings of the 1998 IEEE 6th International Conference on Computer Vision
CityBombay, India

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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