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Usage Notes

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How to use this page

General remarks
  • 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.
Specific remarks
  • Preprocessing:
    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 techniques.
    • 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 implementation ...

    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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