Earticle

초록

영어

A novel framework for highly dynamic scene segmentation through foreground hypothesis is developed here. This framework enables robust foreground segmentation by ranking object hypothesis over spatial space to achieve consistent object candidates and binary segmentation of a video sequence. Inside object candidates derived from spatial features in each frame are first estimated. This is followed by ranking the object candidates over a specific hypothesis space so as to yield consistent and dense object proposals. An efficient higher-order graph-cut method is adapted to optimize a Markov Random Field (MRF) model, which is instantiated by the estimated foreground hypothesis with highest score. We demonstrate the performance of our approach through experimental evaluation on a typical dynamic scene benchmark from Freiburg-Berkeley Motion Segmentation Dataset. Compared with a state-of-the-art algorithm, our method achieves improved and robust segmentation performance when dealing with highly dynamic image sequences. The segmentation accuracy of the proposed method improved by 10.19% and 92.66% pixels are correctly classified.

목차

Abstract
 1. Introduction
 2. Object Candidates Generation
 3. Object Hypotheses Ranking
 4. Spatial-temporal MRF Segmentation
 5. Experiments
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Yinhui Zhang [ Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China, 650500 ]
• Zifen He [ Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China, 650500 ]
• Xing Wu [ Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China, 650500 ]

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