Leng Chengcai, Xiao Jinjun, Li Min, Zhang Haipeng
Key Laboratory of Nondestructive Testing, Ministry of Education, School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang 330063, China ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Information Center, Jiangxi School of Electronics and Information Engineering, Nanchang 330096, China.
Comput Intell Neurosci. 2015;2015:829528. doi: 10.1155/2015/829528. Epub 2015 Apr 19.
This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.
本文提出了一种基于互图矩阵的新型鲁棒自适应主成分分析（RAPCA）方法用于图像配准，以提高鲁棒性和实时性能。贡献可分为三个部分。首先，开发了一种新型RAPCA方法，以基于对象的互图矩阵捕获共同结构模式。其次，基于自适应主成分提出了鲁棒相似性度量。最后，基于RAPCA推导了鲁棒配准算法。实验结果表明，所提出的方法在捕获真实世界图像上的共同结构模式以进行图像配准时非常有效。
