Earticle

초록

영어

In recent years, more and more researchers' attention has been drawn to the sparse representation-based classification (SRC) method and its application in image analysis and pattern recognition, due to its good characteristics of high recognition rate, robustness to corruption and occlusion, and little dependence on the features selection etc. However, sufficient training samples are always required by the sparse representation method for the effective recognition. In practical applications, it is generally difficult to obtain sufficient training samples of the test targets, especially non-cooperative targets. So the key issues in the effective automatic target recognition (ATR) based on the sparse representation are to obtain sufficient training samples in different scales, angles, and different illumination conditions, and to construct an overcomplete dictionary with discriminative ability. In this paper, a novel sparse representation-based scheme is proposed for the automatic target recognition in the real environment, in which the training samples are drawn from the simulation models of real targets and the overcomplete dictionary is trained using structured sparse learning method. The experimental results show that the proposed method is effective for the automatic target recognition in the practical application, especially, where the desired features of the sparse representation method are kept.

목차

Abstract
 1. Introduction
 2. The Improved Algorithms
  2.1. Sparse Representation based Classification
  2.2. The Dictionary Learning Algorithm
 3. Our Proposed ATR Scheme
  3.1. Samples
  3.2. Dictionary
  3.3. Detection
  3.4. Recognition
 4. Experiments
  4.1. Model Image Acquisition System
  4.2. Tests on the Real Target Dataset
 5. Conclusion and Future Work
 Acknowledgements
 References

키워드

저자

• Bo Sun [ School of Information Science and Technology, Beijing Normal University, Beijing 100875, China ]
• Xuewen Wu [ School of Information Science and Technology, Beijing Normal University, Beijing 100875, China ]
• Jun He [ School of Information Science and Technology, Beijing Normal University, Beijing 100875, 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