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Conference proceeding
Object Matching with a Locally Affine-Invariant Constraint
2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp 1641-1648
01 Jan 2010
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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.
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Details
- Title
- Object Matching with a Locally Affine-Invariant Constraint
- Creators
- Hongsheng Li - Lehigh UniversityEdward Kim - Lehigh UniversityXiaolei Huang - Lehigh UniversityLei He - National Institutes of Health
- Publication Details
- 2010 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), pp 1641-1648
- Series
- IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Number of pages
- 8
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000287417501087
- Scopus ID
- 2-s2.0-77955991060
- Other Identifier
- 991021884691504721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Software Engineering
- Imaging Science & Photographic Technology
- Mathematics, Applied