Earticle

초록

영어

An image stitching algorithm based on the robustness of principal component analysis (RPCA) is proposed in an effort to suppress the influence of noise in the image stitching quality. This algorithm represents high dimensional feature data by utilizing a lower dimensional linear subspace, and converts the image stitching problem into a principal component matrix matching problem. Through the use of a low rank matrix, the extraction of salient image characteristics is recovered and the noise interference is reduced during the enhancement process. Together, with the advantages of the RPCA algorithm, the algorithm improves the PSNR of the image while maintaining its strong matching ability. Experimental results show that the proposed scheme is able to significantly inhibit the noise and improve the stitching quality in comparison to the other existing stitching methods.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Robust Principal Component Analysis
 3. Proposed Method
 4. Experimental Results
  4.1. Qualitative Analysis
  4.2. Quantitative Analysis
 5. Conclusion and Future Work
 References

키워드

저자

• Wei Tao [ National Digital Switching System Engineering & Technological R&D Center, Zhengzhou Henan, China / Henan Institute of Engineering, Computer College, Zhengzhou Henan, China ]
• Zhang Yongxin [ Luoyang Normal University, Luoyang Henan, China ]
• Yuan Yating [ School of Information Science and Technology, Northwest University, Xi 'an Shaanxi, China ]
• Ji Xinsheng [ National Digital Switching System Engineering & Technological R&D Center, Zhengzhou Henan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605