2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (2014)
July 13, 2014 to July 15, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PAAP.2014.17
Feature matching is a critical and challenging process in feature-based image registration. In this paper, a robust feature point matching method, combined Scale-Invariant Feature Transform (SIFT) and Support Vector Machine (SVM), is proposed to match features for registering dynamic aerial images. In this method, every feature point is described by 128 dimensional SIFT descriptor as a training vector. Then feature matching model is built by SVM. Using this model, feature points are classified into two categories, one is matched feature set and the other is unmatched feature set. Three pairs of infrared (IR) and ultraviolet (UV) aerial images are utilized to evaluate the performance. The matching results have confirmed that the proposed method can match the feature points exactly even with a lot of outliers.
Feature extraction, Heuristic algorithms, Image registration, Robustness, Support vector machine classification, Algorithm design and analysis,SVM, feature matching, Aerial images, matching model
Zhaoxia Liu, Yaxuan Wang, Yu Jing, Oujun Lou, "A Robust Feature Point Matching Method for Dynamic Aerial Image Registration", 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), vol. 00, no. , pp. 144-147, 2014, doi:10.1109/PAAP.2014.17