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Alpha-matte-based depth map enhancement for hairy objects

Alpha-matte-based depth map enhancement for hairy objects

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  • Author(s): J.-H. Cho 1 ; K.-J. Yoon 2 ; K.H. Lee 1
    • View affiliations
    • Affiliations: 1: Intelligent Design and Graphics Laboratory, Department of Mechatronics, Gwangju Institute of Science of Technology, Gwangju, Korea
      2: Computer Vision Laboratory, Department of Information and Communications, Gwangju Institute of Science of Technology, Gwangju, Korea
  • Source: Volume 46, Issue 3, 4 February 2010, p. 211 – 213
    DOI:  10.1049/el.2010.1909 , Print ISSN 0013-5194, Online ISSN 1350-911X
© The Institution of Engineering and Technology
Published

Most existing depth estimation methods generate an erroneous depth map when a foreground object contains hairy boundaries and this error is very critical in many applications. To solve this problem, a new depth map enhancement method is proposed. The given depth map is enhanced by propagating depth values of the neighbourhood based on an alpha matte. The experiment results show that the boundary of a hairy object is significantly refined by the proposed method.

Inspec keywords: image enhancement; computer graphics

Other keywords: computer vision; alpha matte based depth map enhancement; depth estimation methods; hairy objects

Subjects: Graphics techniques; Optical, image and video signal processing; Computer vision and image processing techniques

References

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      • Levin, A., Lischinski, D., Weiss, Y.: `A closed form solution to natural image matting', Proc. IEEE CVPR, 2006, New York, USA, p. 61–68.
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      • W. Xiong , H.S. Chung , J. Jia . Fractional stereo matching using expectation-maximisation. IEEE Trans. Pattern Anal. Mach. Intell. , 3 , 428 - 443
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      • Tappen, M.F., Freeman, W.T.: `Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters', Proc. IEEE ICCV, 2003, Nice, France, p. 900–907.
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      • R. Szeliski . A comparative study of energy minimisation methods for Markov random fields with smoothness-based priors. IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 1068 - 1080
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