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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