How to use this page
- Fill in the appropriate data in all the form-fields of the previous page.
We already filled in an example that should work.
- After submitting your job, our server will try to fetch your images,
convert them into an internal floating-point image format, do some
preliminary checks and finally start the calculations.
- You will be shown a log-file of your request, where you can watch this
process. The results will simply be attached to the end of the log-file.
- If your browser can not handle automatic updates (most browsers can),
you have to push the RELOAD-button of your browser once in a while to get
the current status.
- After your calculations are finished, the results are stored at our
server for further reference. However, after one or two days, the data
will no longer be available to you.
The implemented network has a specific fusion range,
similar to human stereo vision. Before disparity calculations, your stereo
images have to be aligned accordingly.
You can select either:
- Automatic Vergence Control: the algorithm tries
to align the stereo images automatically. This might fail, than
try "Fixed Disparity Range" instead. Total disparity range
sets the fusion range of the network, and is restricted to maximal
20 pixels. In some cases you might get better results if you
disable Allow for rotation, which constrains the vergence
system to use only horizontal or vertical panning for alignment.
- Fixed Disparity Range: if you know that your images
have no vertical disparity component, and if you know the range
of horizontal disparities in advance, this is the setting for you.
- Disparity Detector:
The different units
implement various methods for disparity estimation. Try any one of these!
Gradient-based: Uses standard optical flow estimation
Tensor-based: Based on an algorithm for the estimation of
texture orientation. It searches for an axis through the origin of
the Fourier power spectrum with minimal inertia.
Gradient-based, iterative: iteratively refines intial
disparity-estimate. Is very precise, but is not really needed with
coherence-based stereo. In addition, it runs slow.
Robust Gradient: Basically identical to the gradient-based detector,
except the estimation stage; here, a robust estimator is used instead of the
usual least-square estimator.
Complex-Cell: calculates motion-energy for various
disparities, and selects the one with strongest response. Might also be
realized by neural circuitry in human visual cortex.
Windowed Fourier-Phase: utilizes the Fourier-Shift-Theorem
to calculate the disparity out of local phase-shifts; very simple
When things go wrong
- Most problems occur in accessing your data by our server. We can
handle http-, ftp- and gopher-protocol. Be sure to
give a correct URL. You might
check the URL you supplied to our software by opening this location in your
own browser. But be sure to check by opening over the WEB, not
locally. If your browser can fetch your data, the URL should be fine. If our
server still can't fetch your data, please contact us.
- The algorithms have been designed to be fairly bullet-proof, but if you
do not enter correct values, you won't get any sensible results.
- This server is used also other purposes. If a certain load-average is
exceeded, this service is temporarily suspended.
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