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Proceedings Paper

Optical systems for task-specific compressive classification
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Paper Abstract

Advancements in machine learning (ML) and deep learning (DL) have enabled imaging systems to perform complex classification tasks, opening numerous problem domains to solutions driven by high quality imagers coupled with algorithmic elements. However, current ML and DL methods for target classification typically rely upon algorithms applied to data measured by traditional imagers. This design paradigm fails to enable the ML and DL algorithms to influence the sensing device itself, and treats the optimization of the sensor and algorithm as separate sequential elements. Additionally, this current paradigm narrowly investigates traditional images, and therefore traditional imaging hardware, as the primary means of data collection. We investigate alternative architectures for computational imaging systems optimized for specific classification tasks, such as digit classification. This involves a holistic approach to the design of the system from the imaging hardware to algorithms. Techniques to find optimal compressive representations of training data are discussed, and most-useful object-space information is evaluated. Methods to translate task-specific compressed data representations into non-traditional computational imaging hardware are described, followed by simulations of such imaging devices coupled with algorithmic classification using ML and DL techniques. Our approach allows for inexpensive, efficient sensing systems. Reduced storage and bandwidth are achievable as well since data representations are compressed measurements which is especially important for high data volume systems.

Paper Details

Date Published: 7 September 2018
PDF: 9 pages
Proc. SPIE 10751, Optics and Photonics for Information Processing XII, 1075108 (7 September 2018); doi: 10.1117/12.2321331
Show Author Affiliations
Gabriel C. Birch, Sandia National Labs. (United States)
Tu-Thach Quach, Sandia National Labs. (United States)
Meghan Galiardi, Sandia National Labs. (United States)
Charles F. LaCasse, Sandia National Labs. (United States)
Amber L. Dagel, Sandia National Labs. (United States)


Published in SPIE Proceedings Vol. 10751:
Optics and Photonics for Information Processing XII
Abdul A. S. Awwal; Khan M. Iftekharuddin; Mireya García Vázquez, Editor(s)

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