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International Conference on Multimedia Modeling

MMM 2018: MultiMedia Modeling pp 67–80Cite as

AGO: Accelerating Global Optimization for Accurate Stereo Matching

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10704))

Abstract

In stereo matching, global algorithms could produce more accurate disparity estimation than aggregated ones. Unfortunately, they remain facing prohibitively high computational challenges while minimizing an energy function. Although various computationally tractable optimizers such as Belief Propagation (BP), Graph Cut (GC) and Dynamic Programming (DP) exist, they still time-consumed and perform a relative higher energy. On one hand, too many intermediate parameters are required for constructing the energy function. In fact, they are trivial, difficult to compute and inevitably bring noises because of their approximate representations. That signifies a simplified energy function structure with fewer intermediate parameters may not only accelerate the running time but also provide greater result while utilizing same optimizer. On the other hand, optimizers are usually designed artificially and also generate approximate solutions. Based on this observation, a suboptimal energy is probably obtained. To alleviate these limitations, an Accelerating Global Optimization (AGO) stereo matching algorithm is proposed in this paper. Integrating the key ideas of Two-Step Global Optimization (TSGO), the modeled energy function of AGO possesses fewer intermediate parameters. What’s more, a refinement term is augmented into the message passing formula of Sequential Tree-Reweighted (TRW-S) optimizer to lower down the energy. Performance evaluations on Middlebury v.2 & v.3 stereo data sets demonstrate that the proposed AGO outperforms than other five challenging stereo algorithms; and also performs better on Microsoft i2i stereo videos. At last, thanks to the simple energy function model of AGO; it shows a faster execution time.

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Acknowledgements

This research has been supported by National Natural Science Foundation of China (U1509207, 61325019, 61472278, 61403281 and 61572357).

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Authors and Affiliations

  1. Key Laboratory of Computer Vision and System (Ministry of Education), Tianjin University of Technology, Tianjin, China

    Peng Yao, Hua Zhang, Yanbing Xue & Shengyong Chen

  2. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China

    Peng Yao, Hua Zhang, Yanbing Xue & Shengyong Chen

Authors
  1. Peng Yao
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  2. Hua Zhang
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  3. Yanbing Xue
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  4. Shengyong Chen
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Corresponding author

Correspondence to Yanbing Xue .

Editor information

Editors and Affiliations

  1. Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Klaus Schoeffmann

  2. Chulalongkorn University, Bangkok, Thailand

    Thanarat H. Chalidabhongse

  3. City University of Hong Kong, Hong Kong, China

    Chong Wah Ngo

  4. Chulalongkorn University, Bangkok, Thailand

    Supavadee Aramvith

  5. Dublin City University, Dublin, Ireland

    Noel E. O’Connor

  6. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  7. Tampere University of Technology, Tampere, Finland

    Moncef Gabbouj

  8. Rutgers University, Piscataway, New Jersey, USA

    Ahmed Elgammal

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Yao, P., Zhang, H., Xue, Y., Chen, S. (2018). AGO: Accelerating Global Optimization for Accurate Stereo Matching. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-73603-7_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73602-0

  • Online ISBN: 978-3-319-73603-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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