Flood Lighting |
Polarized Light Striping |
Comparison |
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Polarized light striping versus flood-lighting.
In this experiment, the scene is
comprised of objects immersed in murky water. Using the polarized light
striping approach, we can control the light transport before image
formation for capturing the same scene with better color and contrast.
Click on the above images for high-resolution versions.
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Poor visibility conditions due to
murky water, bad weather, dust and smoke severely impede the performance of
vision systems. Passive methods have been used to restore scene contrast
under moderate visibility by digital postprocessing.
However, these methods are ineffective when the quality of acquired images
is poor to begin with. In this work, we design active lighting and sensing
systems for controlling light transport before image formation, and hence
obtain higher quality data. First, we present a technique of polarized
light striping based on combining polarization imaging and structured light
striping. We show that this technique out-performs different existing
illumination and sensing methodologies. Second, we present a numerical
approach for computing the optimal relative sensor-source position, which
results in the best quality image. Our analysis accounts for the limits
imposed by sensor noise.
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Publications
"On Controlling Light Transport in Poor Visibility Environments"
Mohit Gupta, SG Narasimhan, YY Schechner,
IEEE Computer Vision and Pattern Recognition (CVPR),
2008.
[PDF]
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Pictures
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Experimental Setup:
Our experimental setup consisting of a glass tank, filled with moderate
to high concentrations of milk. An LCD projector illuminates the medium with
polarized light. The camera (with a polarizer attached) observes a
contrast chart through the medium.
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Comparison of various illumination and sensing techniques:
We compare the performance of various
techniques such as flood-lighting, polarized flood-lighting, light
striping and polarized light-striping. We consider moderate and heavy
scattering conditions. We can notice improvement in contrast using our
technique of polarized light-striping over previous techniques. See full
size image to avoid breaking-up text.
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Limitations of the high-frequency
illumination based method:
In the presence of moderate to heavy
volumetric scattering, the direct component images have low SNR. The
global image is approximately the same as a flood-lit image, and hence,
suffers from low contrast.
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Unpolarized vs. Polarized Light Stripe Scanning:
Using
polarization reduces backscatter, thereby enabling reliable detection of
the intersection of light sheet with the object. Thus, we can improve
image contrast considerably using polarization imaging+light
stripe scanning.
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What is the optimal sensor-source separation for flood-lighting?:
Large separation (60 cms) results in heavy image noise. On the other hand,
optimal separation (40 cms) results in a high contrast, low noise image
Both the frames were captured with the same exposure time.
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What
is the optimal light stripe scan for the best image quality?:
Using
computer simulations, we can design the optimal light stripe scan for the
best image quality. The image quality is quantified in terms of the image
contrast, image SNR, and the gradient across the edge of the stripe
resulting from the intersection of the light sheet with the object.
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Acknowledgements
This research was supported in parts by Grants # ONR N00014-08-1-0330,
NSF CAREER IIS-0643628, NSF CCF-0541307 and the US-Israel Binational Science
Foundation (BSF) Grant # 2006384. Yoav Schechner is a Landau Fellow - supported
by the Taub Foundation. Yoav¡¯s work was conducted in the OllendorffMinerva Center.
Minerva is funded through the BMBF.
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