Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | Optics InfoBase Conference Papers |
| Publisher | Optical Society of America |
| ISBN (Print) | 9781557529756 |
| State | Published - 2013 |
| Event | Computational Optical Sensing and Imaging, COSI 2013 - Arlington, VA, United States Duration: Jun 23 2013 → Jun 27 2013 |
Other
| Other | Computational Optical Sensing and Imaging, COSI 2013 |
|---|---|
| Country | United States |
| City | Arlington, VA |
| Period | 6/23/13 → 6/27/13 |
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ASJC Scopus subject areas
- Instrumentation
- Atomic and Molecular Physics, and Optics
Cite this
Compressive stereo cameras for computing disparity maps. / Treeaporn, Vicha; Ashok, Amit; Neifeld, Mark A.
Optics InfoBase Conference Papers. Optical Society of America, 2013.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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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UR - http://www.scopus.com/inward/citedby.url?scp=84898645223&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84898645223
SN - 9781557529756
BT - Optics InfoBase Conference Papers
PB - Optical Society of America
ER -