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- W2988850463 abstract "Recently, a method known as ensemble deep learning invariant hologram classification (EDL-IHC) for classifying of holograms of deformable objects with deep learning network (DLN) has been demonstrated. However DL-IHC requires substantial computational resources to attain near perfect success rate (≥99%). In practice, it is always desirable to have higher success rate with a low complexity DLN. In this paper we propose a low complexity DLN known as ensemble deep learning invariant hologram classification (EDL-IHC). In comparison with DL-IHC, our proposed hologram classifier has promoted the success rate by 2.86% in the classification of holograms of handwritten numerals." @default.
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- W2988850463 date "2019-11-06" @default.
- W2988850463 modified "2023-10-18" @default.
- W2988850463 title "Ensemble convolutional neural network for classifying holograms of deformable objects" @default.
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- W2988850463 doi "https://doi.org/10.1364/oe.27.034050" @default.
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