
Proceedings Paper
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Paper Abstract
How can we design cameras that image selectively in Full Electro-Magnetic (FEM) spectra? Without selective imaging, we cannot use, for example, ordinary tourist cameras to see through fire, smoke, or other obscurants contributing to creating a Visually Degraded Environment (VDE). This paper addresses a possible new design of selective-imaging cameras at firmware level. The design is consistent with physics of the irreversible thermodynamics of Boltzmann’s molecular entropy. It enables imaging in appropriate FEM spectra for sensing through the VDE, and displaying in color spectra for Human Visual System (HVS). We sense within the spectra the largest entropy value of obscurants such as fire, smoke, etc. Then we apply a smart firmware implementation of Blind Sources Separation (BSS) to separate all entropy sources associated with specific Kelvin temperatures. Finally, we recompose the scene using specific RGB colors constrained by the HVS, by up/down shifting Planck spectra at each pixel and time.
Paper Details
Date Published: 2 June 2015
PDF: 9 pages
Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960H (2 June 2015); doi: 10.1117/12.2176093
Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)
PDF: 9 pages
Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960H (2 June 2015); doi: 10.1117/12.2176093
Show Author Affiliations
Harold Szu, The Catholic Univ. of America (United States)
Charles Hsu, The George Washington Univ. (United States)
Joseph Landa, The Catholic Univ. of America (United States)
Charles Hsu, The George Washington Univ. (United States)
Joseph Landa, The Catholic Univ. of America (United States)
Jae H. Cha, Virginia Polytechnic Institute and State Univ. (United States)
Keith A. Krapels, The Univ. of Memphis (United States)
Keith A. Krapels, The Univ. of Memphis (United States)
Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)
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