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Spatiotemporal volume saliency

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Abstract

This paper proposes spatiotemporal volume saliency to detect and explore salient regions in time-varying volume data. Based on the center-surround hypothesis that the salient region stands out from its surroundings, we extend the spatial saliency to time domain and introduce temporal volume saliency. It is defined as a center-surround operator on Gaussian-weighted mean attribute gradient between steps in a scale-independent manner. By combing spatial saliency and temporal saliency together, our spatiotemporal volume saliency is effective in detecting changes of salient regions. We demonstrate its utility in this regard by automating transfer function design and selecting key frames for time-varying volume data.

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Acknowledgments

This research is supported by the National Natural Science Foundation of China under Grant No. 61170157, the National Grand Fundamental Research 973 Program of China under Grant No. G2009CB72380, and the Scientific Research Founding Project of NUDT.

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, Hunan, China

    Enya Shen & Sikun Li

  2. Visual Computing Research Center, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China

    Yunhai Wang

Authors
  1. Enya Shen
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  2. Yunhai Wang
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  3. Sikun Li
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Correspondence to Enya Shen.

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Shen, E., Wang, Y. & Li, S. Spatiotemporal volume saliency. J Vis 19, 157–168 (2016). https://doi.org/10.1007/s12650-015-0293-y

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  • DOI: https://doi.org/10.1007/s12650-015-0293-y

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