Stereo Matching Based on CF-EM Joint Algorithm

  • Baoping Li
  • Long YeEmail author
  • Yun Tie
  • Qin Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)


Cost Filtering (CF) and Energy Minimization (EM) are two main cost aggregation methods in stereo matching. Due to global smoothness assumption, EM methods can get higher matching accuracy. However, they tend to fail in occluded areas, while locally adaptive support-weight CF method can solve it well. This paper proposed a CF-EM joint stereo matching framework on the basis of the proof that CF and EM methods can realize interconversion to each other. In this joint framework, we firstly use CF method with fully connected Markov Random Field (F-MRF) model to yield a more robust unary potential. And then, the output unary potential is used as the input to a standard EM method to compute the final disparity in Local connected MRF (L-MRF) model. Experiments results demonstrate that our method can improve the stereo matching accuracy as the achievement of energy transferring from F-MRF to L-MRF.


Stereo-matching Cost filtering Energy minimization Joint algorithm Belief propagation 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Key Laboratory Media Audio and Video Ministry of EducationCommunication University of ChinaBeijingChina
  2. 2.Henan Polytechnic UniversityJiaozuoChina
  3. 3.Zhengzhou UniversityZhengzhouChina

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