Communication Dans Un Congrès Année : 2021

Using Deep Learning for Object Distance Prediction in Digital Holography

Résumé

Deep Learning (DL) has marked the beginning of a new era in computer science, particularly in Machine Learning (ML). Nowadays, there are many fields where DL is applied such as speech recognition, automatic navigation systems, image processing, etc [1]. In this paper, a Convolutional Neural Network (CNN), more precisely a CNN built on top of DenseNet169, is proven to be helpful in predicting object distance in computer-generated holographic images. The problem is addressed as a classification problem where 101 classes of images were generated, each class corresponding to a different distance value from the object at a micrometer scale. Experiments show that the proposed network is efficient in this context, being able to classify with a 100% accuracy level if trained properly.

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Dates et versions

hal-03551919 , version 1 (02-02-2022)

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  • HAL Id : hal-03551919 , version 1

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Raphael Couturier, Michel Salomon, Elie Abou Zeid, Chady Abou Jaoude. Using Deep Learning for Object Distance Prediction in Digital Holography. International Conference on Computer, Control and Robotics, Jan 2021, Shanghaï, China. ⟨hal-03551919⟩
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