Open AccessEnsemble convolutional neural network for classifying holograms of deformable objects
Author(s) -
Hoson Lam,
P.W.M. Tsang,
TingChung Poon
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.034050
Subject(s) - holography , computer science , artificial intelligence , deep learning , convolutional neural network , pattern recognition (psychology) , invariant (physics) , artificial neural network , classifier (uml) , computational complexity theory , optics , algorithm , mathematics , physics , mathematical physics
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.