Veeraraghavan, AshokPatel, Ankit2020-02-052020-02-052015-122016-10-21December 2Chen, George. "Efficient Machine Vision Using Computational Cameras." (2016) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/108003">https://hdl.handle.net/1911/108003</a>.https://hdl.handle.net/1911/108003Computational 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.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Computational ImagingComputer VisionCompressive SensingDeep LearningThesis2020-02-05