scholarly journals Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Chengcai Leng ◽  
Jinjun Xiao ◽  
Min Li ◽  
Haipeng Zhang

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.

Author(s):  
Ekaterina Aleksandrova ◽  
Christos Anagnostopoulos

This chapter introduces statistical learning methods and findings of a group decision-making algorithm in internet of things (IoT) and edge computing environments. The discussed methodology locally detects outliers in an on-line and adaptive mode. It is driven by three perspectives—opinion, confidence, and independence—and exploits the incremental principal component analysis using the power method for eigenvector and eigenvalue estimation and Knuth and Welford's online algorithms for variance estimation. The methodology is implemented and evaluated over real contextual data in a wireless network of environmental sensors from where appropriate conclusions are drawn about the capabilities and limitations of the proposed solution in IoT environments.


2016 ◽  
Vol 23 (4) ◽  
pp. 434-438 ◽  
Author(s):  
Chenglin Zuo ◽  
Ljubomir Jovanov ◽  
Bart Goossens ◽  
Hiep Quang Luong ◽  
Wilfried Philips ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document