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Salient Object Detection from Single Haze Images via Dark Channel Prior and Region Covariance Descriptor

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Book cover Intelligent Visual Surveillance (IVS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 664))

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Abstract

Due to degraded visibility and low contrast, object detection from single haze images faces great challenges. This paper proposed to use a computational model of visual saliency to cope with this issue. Superpixel-level saliency map is firstly abstracted via the dark channel prior. Then, region covariance descriptors are utilized to estimate local and global saliency of each superpixel. Besides, the graph model is incorporated as constraint to optimize the correlation between superpixels. Experimental results verify the validity and efficiency of the proposed saliency computational model.

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Acknowledgments

This work was supported by the Natural Science Foundation of China (61602349, 61273225, 61273303, and 61403287) and the China Scholarship Council (201508420248).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China

    Nan Mu, Xin Xu & Xiaolong Zhang

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, China

    Xin Xu & Xiaolong Zhang

Authors
  1. Nan Mu
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  2. Xin Xu
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  3. Xiaolong Zhang
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Corresponding author

Correspondence to Xin Xu .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences , Beijing, China

    Zhang Zhang

  2. Chinese Academy of Sciences , Beijing, China

    Kaiqi Huang

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© 2016 Springer Nature Singapore Pte Ltd.

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Mu, N., Xu, X., Zhang, X. (2016). Salient Object Detection from Single Haze Images via Dark Channel Prior and Region Covariance Descriptor. In: Zhang, Z., Huang, K. (eds) Intelligent Visual Surveillance. IVS 2016. Communications in Computer and Information Science, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-3476-3_12

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  • DOI: https://doi.org/10.1007/978-981-10-3476-3_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3475-6

  • Online ISBN: 978-981-10-3476-3

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