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Computer Vision from Spatial-Multiplexing Cameras at Low Measurement Rates


Abstract In UAVs and parking lots, it is typical to first collect an enormous number of pixels using conventional imagers. This is followed by employment of expensive methods to compress by throwing away redundant data. Subsequently, the compressed data is transmitted to a ground station. The past decade has seen the emergence of novel imagers called spatial-multiplexing cameras, which offer compression at the sensing level itself by providing an arbitrary linear measurements of the scene instead of pixel-based sampling. In this dissertation, I discuss various approaches for effective information extraction from spatial-multiplexing measurements and present the trade-offs between reliability of the performance and computational/storage load of the s... (more)
Created Date 2017
Contributor Kulkarni, Kuldeep Sharad (Author) / Turaga, Pavan (Advisor) / Li, Baoxin (Committee member) / Chakrabarti, Chaitali (Committee member) / Sankaranarayanan, Aswin (Committee member) / LiKamWa, Robert (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Action Recognition / Cameras / Compressive Sensing / Computer Vision / Object Tracking / Spatial-Multiplexers
Type Doctoral Dissertation
Extent 106 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Electrical Engineering 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis