Object matching with a locally affine-invariant constraint

Hongsheng Li, Edward Kim, Xiaolei Huang, Lei He

Research output: Chapter in Book/Report/Conference proceedingConference contribution

55 Scopus citations

Abstract

In this paper, we present a new object matching algorithm based on linear programming and a novel locally affine-invariant geometric constraint. Previous works have shown possible ways to solve the feature and object matching problem by linear programming techniques [9], [10]. To model and solve the matching problem in a linear formulation, all geometric constraints should be able to be exactly or approximately reformulated into a linear form. This is a major difficulty for this kind of matching algorithms. We propose a novel locally affine-invariant constraint which can be exactly linearized and requires a lot fewer auxiliary variables than the previous work [10] does. The key idea behind it is that each point can be exactly represented by an affine combination of its neighboring points, whose weights can be solved easily by least squares. The resulting overall objective function can then be solved efficiently by linear programming techniques. Our experimental results on both rigid and non-rigid object matching show the advantages of the proposed algorithm.

Original language English (US) 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 1641-1648 8 https://doi.org/10.1109/CVPR.2010.5539776 Published - 2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United StatesDuration: Jun 13 2010 → Jun 18 2010

Publication series

Name Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1063-6919

Other

Other 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 United States San Francisco, CA 6/13/10 → 6/18/10

All Science Journal Classification (ASJC) codes

• Software
• Computer Vision and Pattern Recognition

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