Efficient Machine Vision Using Computational Cameras
Veeraraghavan, Ashok; Patel, Ankit
Master of Science
Computational cameras, powered by novel optics and advanced signal processing algorithms, has emerged as a powerful imaging tool that brings orders of magni- tude performance improvements over current camera technology. However, existing computer vision pipelines are still built around conventional digital cameras. In this thesis, we propose a novel computer vision framework that integrates computational cameras for machine vision applications. I explore two possible ways of improving the energy-efficiency and cost-effectiveness under such proposed framework. We first introduce ASP Vision, a jointly designed sensor + deep learning system for visual recognition tasks. ASP Vision utilizes angle sensitive pixels (ASP) to optically compute the first layer of convolutional neural networks (CNN), resulting 10x savings in sensing energy and bandwidth, and 2-4% savings in CNN FLOPs, while achieving similar performance compared to traditional deep learning pipelines. We then present FPA-CS, a focal plane array based compressive sensing architecture that provides a 15x cost savings in high-resolution shortwave infrared (SWIR) video acquisition.
Computational Imaging; Computer Vision; Compressive Sensing; Deep Learning