TY - GEN
T1 - Compressive stereo cameras for computing disparity maps
AU - Treeaporn, Vicha
AU - Ashok, Amit
AU - Neifeld, Mark A.
PY - 2013
Y1 - 2013
N2 - Compressive imaging employs the direct measurement of object features and has been shown to offer both performance (e.g., improved reconstructed image fidelity) and cost (e.g., reduced number of measurements relative to the native dimensionality) advantages. We examine compressive imaging within a stereo vision application in which a traditional correspondence algorithm is used to find pixel disparity maps. Through simulation we show that compressive imaging provides sufficient image fidelity with 12.8× compression to compute disparity maps with less that 4.5% error on average at 0.5% relative measurement noise strength.
AB - Compressive imaging employs the direct measurement of object features and has been shown to offer both performance (e.g., improved reconstructed image fidelity) and cost (e.g., reduced number of measurements relative to the native dimensionality) advantages. We examine compressive imaging within a stereo vision application in which a traditional correspondence algorithm is used to find pixel disparity maps. Through simulation we show that compressive imaging provides sufficient image fidelity with 12.8× compression to compute disparity maps with less that 4.5% error on average at 0.5% relative measurement noise strength.
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U2 - 10.1364/cosi.2013.cm1c.4
DO - 10.1364/cosi.2013.cm1c.4
M3 - Conference contribution
SN - 9781557529756
T3 - Optics InfoBase Conference Papers
BT - Computational Optical Sensing and Imaging, COSI 2013
PB - Optical Society of America (OSA)
T2 - Computational Optical Sensing and Imaging, COSI 2013
Y2 - 23 June 2013 through 27 June 2013
ER -