Representation and Self-Similarity of Shapes

Representing shapes is a signi cant 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 axisSA and shape axis tree SA-tree. We start with a shape, its boundary contour, and two di erent 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.

• Type: Conference
• Year: 1998
• Where: ICCV
• Authors: Tyng-Luh Liu, Davi Geiger, Robert Kohn
• Keywords: Boundary Contour, Computer Vision, ICCV 1998, Pairing Contour Points, Self-similarity Criteria, Shape Axis, Shape AxisSA