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Optimizing a Compressive Imager for Machine Learning Tasks

Conference Record - Asilomar Conference on Signals, Systems and Computers

Redman, Brian J.; Calzada, Daniel; Wingo, Jamie; Quach, Tu-Thach Q.; Galiardi, Meghan; Dagel, Amber L.; LaCasse, Charles F.; Birch, Gabriel C.

Images are often not the optimal data form to perform machine learning tasks such as scene classification. Compressive classification can reduce the size, weight, and power of a system by selecting the minimum information while maximizing classification accuracy.In this work we present designs and simulations of prism arrays which realize sensing matrices using a monolithic element. The sensing matrix is optimized using a neural network architecture to maximize classification accuracy of the MNIST dataset while considering the blurring caused by the size of each prism. Simulated optical hardware performance for a range of prism sizes are reported.

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TYPE Conference Poster YEAR 2019
ScopusOSTIDOI
16 Results
16 Results