computer-vision
Camouflaged Object Segmentation
3260 papers • 126 benchmarks • 313 datasets
Camouflaged object segmentation (COS) or Camouflaged object detection (COD), which was originally promoted by T.-N. Le et al. (2017), aims to identify objects that conceal their texture into the surrounding environment. The high intrinsic similarities between the target object and the background make COS/COD far more challenging than the traditional object segmentation task. Also, refer to the online benchmarks on CAMO dataset, COD dataset, and online demo. ( Image source: Anabranch Network for Camouflaged Object Segmentation )
(Image credit: Papersgraph)
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PCOD_1200
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CAMO
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iUse these libraries to find camouflaged-object-segmentation-2 models and implementations
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UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zongwei Zhou, M. R. Siddiquee, Nima Tajbakhsh, Jianming Liang•Tue Jul 17 2018
This paper presents UNet++, a new, more powerful architecture for medical image segmentation where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways, and argues that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar.
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