Earticle

초록

영어

In order to increase the effect of matching for local stereo matching method and decrease the amount of computation, a new adaptive weight based local stereo matching method is proposed in this paper. In this method, two methods are mainly employed to construct weight model: (1) Neural Network is used to establish the spatial weight model, which makes good use of the pixels in support window; (2) An edge hold off Mean-Shift method is proposed to distribute the intensity weight accurately. For decreasing the matching cost error, the census transform is introduced to calculate the matching cost. The influence of the parameters on the performance of our method is also discussed at last. Simulation results indicate that the performance of our method is better than that of Yoon’s method under low support window.

목차

Abstract
 1. Introduction
 2. Improved Cost Aggregation Function
  2.1. Spatial Weight Algorithm Based on Neural Network
  2.2. Intensity Weight Function based on Edge Hold off Mean-Shift Algorithm
  2.3. Aggregation Cost
 3. LBP Algorithm
 4. Simulation Experiment and Discussions
  4.1. Effect of Parameter a on Performances of Our Algorithm
  4.2. Influence of Segmentation Algorithm in Performance
  4.3. Comparison with Yoon Algorithm
  4.4. LBP Algorithm Energy Change
  4.5. Performance Evaluation
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Zheng Sun [ School of Mechatronic Engineering, Zaozhuang University, Zaozhuang 277160 ]
• Rong Yang [ Department of Mechanical Engineering, McMaster University, ON L8S 1A1 ]
• Hui Li [ School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116 ]

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