Oktar Y.Ulucan O.Karakaya D.Ersoy E.O.Türkan, Mehmet2023-06-162023-06-1620209.78E+12https://doi.org/10.1109/SIU49456.2020.9302144https://hdl.handle.net/20.500.14365/361628th Signal Processing and Communications Applications Conference, SIU 2020 -- 5 October 2020 through 7 October 2020 -- 166413It is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the human brain. As human visual system works in binocular, the collaboration of the eyes with the two brain lobes needs more investigation for improvements in such CNN-based visual imagery analysis applications. It is indeed questionable that if respective visual fields of each eye and the associated brain lobes are responsible for different learning abilities of the same scene. There are such open questions in this field of research which need rigorous investigation in order to further understand the nature of the human visual system, hence improve the currently available deep learning applications. This paper analyses a binocular CNNs architecture that is more analogous to the biological structure of the human visual system than the conventional deep learning techniques. While taking a structure called optic chiasma into account, this architecture consists of basically two parallel CNN structures associated with each visual field and the brain lobe, fully connected later possibly as in the primary visual cortex. Experimental results demonstrate that binocular learning of two different visual fields leads to better classification rates on average, when compared to classical CNN architectures. © 2020 IEEE.trinfo:eu-repo/semantics/closedAccessBinocular visionConvolutional neural networksDeep learningDeep neural networksHuman visual systemBinocular visionBinocularsConvolutionDeep learningImage enhancementLearning systemsNetwork architectureStereo image processingBiological neural networksBiological structuresClassification ratesConvolutional networksHuman Visual SystemLearning abilitiesLearning techniquesPrimary visual cortexConvolutional neural networksConference Object10.1109/SIU49456.2020.93021442-s2.0-85100292107