Passive ranging redundancy reduction in diurnal weather conditions

Jae H. Cha; A. Lynn Abbott; Harold H. Szu

doi:10.1117/12.2018433

29 May 2013 Passive ranging redundancy reduction in diurnal weather conditions

Jae H. Cha, A. Lynn Abbott, Harold H. Szu

Proceedings Volume 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI; 87500F (2013) https://doi.org/10.1117/12.2018433
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Ambiguity in binocular ranging (David Marr’s paradox) may be resolved by using two eyes moving from side to side behind an optical bench while integrating multiple views. Moving a head from left to right with one eye closed can also help resolve the foreground and background range uncertainty. That empirical experiment implies redundancy in image data, which may be reduced by adopting a 3-D camera imaging model to perform compressive sensing. Here, the compressive sensing concept is examined from the perspective of redundancy reduction in images subject to diurnal and weather variations for the purpose of resolving range uncertainty at all weather conditions such as the dawn or dusk, the daytime with different light level or the nighttime at different spectral band. As an example, a scenario at an intersection of a country road at dawn/dusk is discussed where the location of the traffic signs needs to be resolved by passive ranging to answer whether it is located on the same side of the road or the opposite side, which is under the influence of temporal light/color level variation. A spectral band extrapolation via application of Lagrange Constrained Neural Network (LCNN) learning algorithm is discussed to address lost color restoration at dawn/dusk. A numerical simulation is illustrated along with the code example.

Citation Download Citation

Jae H. Cha, A. Lynn Abbott, and Harold H. Szu "Passive ranging redundancy reduction in diurnal weather conditions", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 87500F (29 May 2013); https://doi.org/10.1117/12.2018433

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