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Proceedings Paper

Point matching based on non-parametric model
Author(s): Renfeng Liu; Cong Zhang; Jinwen Tian
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Paper Abstract

Establishing reliable feature correspondence between two images is a fundamental problem in vision analysis and it is a critical prerequisite in a wide range of applications including structure-from-motion, 3D reconstruction, tracking, image retrieval, registration, and object recognition. The feature could be point, line, curve or surface, among which the point feature is primary and is the foundation of all features. Numerous techniques related to point matching have been proposed within a rich and extensive literature, which are typically studied under rigid/affine or non-rigid motion, corresponding to parametric and non-parametric models for the underlying image relations. In this paper, we provide a review of our previous work on point matching, focusing on nonparametric models. We also make an experimental comparison of the introduced methods, and discuss their advantages and disadvantages as well.

Paper Details

Date Published: 14 December 2015
PDF: 8 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130X (14 December 2015); doi: 10.1117/12.2205409
Show Author Affiliations
Renfeng Liu, Wuhan Polytechnic Univ. (China)
Cong Zhang, Wuhan Polytechnic Univ. (China)
Jinwen Tian, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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