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Occluded Object Imaging Based on Collaborative Synthetic Aperture Photography Read chapter Read first chapter

2016 | OriginalPaper | Chapter

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

Authors : Nan Mu, Xin Xu, Xiaolong Zhang

Published in: Intelligent Visual Surveillance

Publisher: Springer Singapore

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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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Metadata
Title
Salient Object Detection from Single Haze Images via Dark Channel Prior and Region Covariance Descriptor
Authors
Nan Mu
Xin Xu
Xiaolong Zhang
Copyright Year
2016
Publisher
Springer Singapore
Book
Intelligent Visual Surveillance

Print ISBN: 978-981-10-3475-6

Electronic ISBN: 978-981-10-3476-3

Copyright Year: 2016

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

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