We present a scalable information-optimal compressive imager optimized for the target classification task, discriminating between two target classes. Compressive projections are optimized using the Cauchy-Schwarz Mutual Information (CSMI) metric, which provides an upper-bound to the probability of error of target classification. The optimized measurements provide significant performance improvement relative to random and PCA secant projections. We validate the simulation performance of information-optimal compressive measurements with experimental data.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/621547 |
| Date | 20 May 2016 |
| Creators | Kerviche, Ronan, Ashok, Amit |
| Contributors | Univ Arizona, Coll Opt Sci, Univ Arizona, Dept Elect & Comp Engn, College of Optical Sciences, The Univ. of Arizona (United States), College of Optical Sciences, The Univ. of Arizona (United States) |
| Publisher | SPIE-INT SOC OPTICAL ENGINEERING |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Article |
| Rights | © 2016 SPIE |
| Relation | http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2228570 |
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