Representation and Self-Similarity of Shapes
Abstract
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, of 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.
Cite
Text
Liu et al. "Representation and Self-Similarity of Shapes." IEEE/CVF International Conference on Computer Vision, 1998. doi:10.1109/ICCV.1998.710858Markdown
[Liu et al. "Representation and Self-Similarity of Shapes." IEEE/CVF International Conference on Computer Vision, 1998.](https://mlanthology.org/iccv/1998/liu1998iccv-representation/) doi:10.1109/ICCV.1998.710858BibTeX
@inproceedings{liu1998iccv-representation,
title = {{Representation and Self-Similarity of Shapes}},
author = {Liu, Tyng-Luh and Geiger, Davi and Kohn, Robert},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1998},
pages = {1129-1138},
doi = {10.1109/ICCV.1998.710858},
url = {https://mlanthology.org/iccv/1998/liu1998iccv-representation/}
}