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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.
  • 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
  • Main URL:
    This points to the location of your data. Typically, stereo images are stored as one single chunk of data, but in various different formats. Choose the appropriate format for conversion. The table below list all available formats:

    Conversion Type Image arrangement
    2 Images This is the standard arrangment. You have to supply two URLs; one for the left image, one (in the secondary input-field) for the right image.
    Left|Right The images are arranged for parallel viewing; this means the left stereo image is left, the right stereo image left.
    Right|Left The images are arranged for crossed-eye viewing; thus the left stereo image is right, the right stereo image is left.
    Mirror Some stereo images are arranaged for viewing with a small mirror; one of the images seems to be the mirror image of the other (actually, it's not - otherwise you wouldn't see 3D...)
    Anaglyph This one is (obviously) for anaglyph stereo images. In these images, the left data is found in the red color channel, the right stereo data in the green or blue channel.
    Right|Left|Right Some stereo images are arranged in this order, to allow simultaniously parallel and crossed-eye viewing.
    Right/Left Here, the stereo images are stacked vertically above each other. The right image is on top, the left image on the bottom of the stereo picture.
    Left/Right The left stereo image is vertically stacked above the right stereo image.
    Interlaced Both stereo images are interlaced; even horizontal image rows correspond to the left, odd horizontal image rows to the right stereo image.
    Sirds Image is assumed to be a Single Image Random-Dot Stereogram. The algorithm tries the see the 3D-effect in the SIRD, but note that the implementation might encounter the same difficulties as you by viewing such images, i.e., not seeing any depth ...

    If you specify "Two Images", you have to fill in the secondary URL with the pointer to the right image data.

    Normally, horizontal disparities are calculated. However, if your images have vertical disparities, click on the appropriate button.

  • Maximal Image Size:
    The images are zoomed down to this image-size before starting processing.

  • 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: this sets the fusion range of the network. The fusion range of the network is restricted to maximal +-20 pixel.
      • Allow for rotation: the vergence system includes a cyclovergence unit. In some cases, you might get better results if you disable that. The vergence system uses then only horizontal or vertical panning for alignment.
      • Focus: specifies the area of the image which is considered "important" by the vergence system. For images with mainly frontoparallel surfaces, try the "wide"-setting; for small objects in the center of the stereo images, try "narrow".

    • 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.

  • Spatial Scales used:
    Like in human stereo vision, data is processed in parallel at several spatial scales. Since this is a serial machine, selecting less spatial scales speeds up processing. Using more spatial scales gives smoother results, but the improvement is not dramatic.

  • 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 ...

  • Coherence-Detection:
    The coherence stage is the most important stage of the new algorithm. Since it's a parallel algorithm implemented on a normal computer, two different implementations are available:
    • Fast Sync-Scheme: Data from the coarser spatial channels is simply included in the disparity stack of the finest resolution layer, where the coherence-detection is performed. This is simple and fast.
    • Hierarchical Sync-Scheme: This processing option is more adaptive - it takes into account that disparity data from coarser spatial channels is less precise than from finer spatial channels (due the spatial averaging in the coarse spatial channels). Gives better results, but takes longer. If spatial coherence is also included, the results improve a little bit - but the algorithm runs longer.

    • Verify Data: If set to a value larger than 0.0, the disparity map is set to zero wherever the validation map is below that threshold. Use values between 0.0 and 1.0 units.

  • Output-Format:
    Your can choose between different output-formats:
    • Colored Disparity Map : with this output format, the disparity map is converted into a shaded color image, where different colors decoded depth. Takes long, but can display very fine depth-variations.
    • Grayscale Display : this output format gives back the various calculated maps without any conversion applied.
    • Red_blue Anaglyph : your stereo image is converted into an anaglyph which may be viewed with a red filter in front of the left eye, and a blue (or green) filter in front of the right eye.
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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