Towards a convolutional neural network coupled millimetre-wave coded aperture image classifier system

Rahul Sharma; The Viet Hoang; Bhabesh Deka; Vincent Fusco; Okan Yurduseven

doi:10.1117/12.2587470

12 April 2021 Towards a convolutional neural network coupled millimetre-wave coded aperture image classifier system

Rahul Sharma, The Viet Hoang, Bhabesh Deka, Vincent Fusco, Okan Yurduseven

Proceedings Volume 11745, Passive and Active Millimeter-Wave Imaging XXIV; 117450C (2021) https://doi.org/10.1117/12.2587470
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Computational millimetre-wave (mmW) imaging and machine learning have followed parallel tracks since their inception. Recent developments in computational imaging (CI) have significantly improved the imaging capabilities of mmW imaging systems. Machine learning algorithms have also gained huge popularity among researchers in the recent past with several approaches being investigated to make use of them in imaging systems. One such algorithm, image classifier, has gained significant traction in applications such as security screening and traffic surveillance. In this article, we present the first steps towards a machine learning integrated CI physical model for image classification at mmW frequencies. The dataset used for training CI system is generated using the developed single-pixel CI forward-model, eliminating the need for traditional raster-scanning based imaging techniques.

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Citation Download Citation

Rahul Sharma, The Viet Hoang, Bhabesh Deka, Vincent Fusco, and Okan Yurduseven "Towards a convolutional neural network coupled millimetre-wave coded aperture image classifier system", Proc. SPIE 11745, Passive and Active Millimeter-Wave Imaging XXIV, 117450C (12 April 2021); https://doi.org/10.1117/12.2587470

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