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3D Video Based Segmentation and Motion Estimation with Active Surface Evolution

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Abstract

In this paper, a novel method for three-dimensional (3D) segmentation and motion estimation based on 3D videos provided by TOF cameras is presented. The problem is formulated by a variational statement derived from the maximum a posterior probability (MAP) using 3D Optical Flow Constraint, containing both evolution surface and motion parameters. Therefore, the proposed method allows them to benefit from each other and perform motion segmentation and estimation simultaneously. All the formulation is under the assumption that environmental objects are rigid, and an iterative, PDE-driven level set method is adopted for energy minimization. Various experimental results show the validity of the proposed algorithm.

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Authors and Affiliations

  1. Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

    Shiyan Wang, Huimin Yu & Roland Hu

Authors
  1. Shiyan Wang
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  2. Huimin Yu
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  3. Roland Hu
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Corresponding author

Correspondence to Huimin Yu.

Additional information

This work was supported by NSFC under Grant No.60872069 and a National Key Basic Research Project of China (973 Program No.2012CB316400).

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Wang, S., Yu, H. & Hu, R. 3D Video Based Segmentation and Motion Estimation with Active Surface Evolution. J Sign Process Syst 71, 21–34 (2013). https://doi.org/10.1007/s11265-012-0675-5

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  • DOI: https://doi.org/10.1007/s11265-012-0675-5

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