Shape analysis of planar objects with arbitrary topologies using conformal geometry

  • Authors:

  • Lok Ming Lui;Wei Zeng;Shing-Tung Yau;Xianfeng Gu

  • Affiliations:

  • Department of Mathematics, Harvard University, Cambridge, MA;Department of Computer Science, Wayne State University, Detroit, MI and Department of Computer Science, SUNY Stony Brook, Stony Brook, NY;Department of Mathematics, Harvard University, Cambridge, MA;Department of Computer Science, SUNY Stony Brook, Stony Brook, NY

  • Venue:
  • ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

The study of 2D shapes is a central problem in the field of computer vision. In 2D shape analysis, classification and recognition of objects from their observed silhouettes are extremely crucial and yet difficult. It usually involves an efficient representation of 2D shape space with natural metric, so that its mathematical structure can be used for further analysis. Although significant progress has been made for the study of 2D simply-connected shapes, very few works have been done on the study of 2D objects with arbitrary topologies. In this work, we propose a representation of general 2D domains with arbitrary topologies using conformal geometry. A natural metric can be defined on the proposed representation space, which gives a metric to measure dissimilarities between objects. The main idea is to map the exterior and interior of the domain conformally to unit disks and circle domains, using holomorphic 1-forms. Aset of diffeomorphisms from the unit circle S1 to itself can be obtained, which together with the conformal modules are used to define the shape signature. We prove mathematically that our proposed signature uniquely represents shapes with arbitrary topologies. We also introduce a reconstruction algorithm to obtain shapes from their signatures. This completes our framework and allows us tomove back and forth between shapes and signatures. Experiments show the efficacy of our proposed algorithm as a stable shape representation scheme.