Translator Disclaimer
14 December 2015 Point matching based on non-parametric model
Author Affiliations +
Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130X (2015)
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Renfeng Liu, Cong Zhang, and Jinwen Tian "Point matching based on non-parametric model", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130X (14 December 2015);


Back to Top