Object classification through a single-pixel detector

Machine vision systems have many applications, including self-driving cars, intelligent manufacturing, robotic surgery and biomedical imaging, among many others. Most of these machine vision systems use lens-based cameras, and after an image or video is captured, typically with a few megapixels per frame, a digital processor is used to perform machine-learning tasks, such as object classification and scene segmentation. Such a traditional machine vision architecture suffers from several drawbacks. First, the large amount of digital information makes it hard to achieve image/video analysis at high speed, especially using mobile and battery-powered devices. In addition, the captured images usually contain redundant information, which overwhelms the digital processor with a high computational burden, creating inefficiencies in terms of power and memory requirements. Moreover, beyond the visible wavelengths of light, fabricating high-pixel-count image sensors, such as what we have in our mobile phone cameras, is challenging and expensive, which limits the applications of standard machine vision methods at longer wavelengths, such as terahertz part of the spectrum.

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