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Camouflaged Object Segmentation in Images

Yan, Jinnan
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1576064709283297

Abstract Details

2019, Master of Computer Science (M.C.S.), University of Dayton, Computer Science.
Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this thesis, we propose a novel bio-inspired network, named the CamoNet, that leverages both instance segmentation and adversarial attack for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the adversarial stream corresponding with the original image and its flipped image, respectively. The output from the adversarial stream is then fused into the main stream’s result for the final camouflage map to boost up the segmentation accuracy. We also introduce the Data Augmentation in the Wild to solve the data insufficiency for network training. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, significantly outperforming the state-of-the-arts.
Tam Nguyen (Advisor)
42 p.
Computer Science
Camouflaged Object Segmentation

Recommended Citations

Citations

  • Yan, J. (2019). Camouflaged Object Segmentation in Images [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1576064709283297

    APA Style (7th edition)

  • Yan, Jinnan. Camouflaged Object Segmentation in Images. 2019. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1576064709283297.

    MLA Style (8th edition)

  • Yan, Jinnan. "Camouflaged Object Segmentation in Images." Master's thesis, University of Dayton, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1576064709283297

    Chicago Manual of Style (17th edition)

dayton1576064709283297
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This open access ETD is published by University of Dayton and OhioLINK.