Earticle

초록

영어

A multiple moving object segmentation algorithm based on Background Modeling and Adaptive Clustering (named as BMAC) algorithm is proposed in this paper. For moving object segmentation, the algorithm uses Chebyshev inequality and the kernel density estimation method to do background modeling firstly. Then in order to classify image pixels as background points, foreground points and suspicious points, an adaptive threshold algorithm is proposed accordingly. After using background modeling, adaptive clustering is used for multi-object segmentation. It defines pixel space connectivity rate and designs a perpendicular split method, initial cluster adaptive splitting and merging self-organizing the iterative clustering segmentation algorithm, without pre-set number of clustering, completes multi-object segmentation for the foreground image. The segmentation results are consistent with the human visual judgment, the use of space connectivity information improve the accuracy of clustering segmentation, comparison and analysis the experimental results show that the proposed algorithm is feasible, rapid and effective.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Background Modeling and Adaptive Clustering Algorithm
 4. Experimental Results
 5. Conclusion
 References

키워드

저자

• Zhengyi Hu [ College of Mechanical Science and Engineering, Jilin University, Changchun, 130000, China, Changchun automobile industry institute, Changchun, 130000, China ] Corresponding Author
• Qingchang Tan [ College of Mechanical Science and Engineering, Jilin University, Changchun, 130000, China ]
• Kun Zhang [ Northeastern University, 110084, Liaoning, China ]
• Xin Wang [ Northeastern University, 110084, Liaoning, 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