A novel algorithm was developed to estimate scene depth by uniquely exploiting plenoptic image data. Plenoptic images allow multiple perspectives to be extracted from a single exposure from a single lens. Depth from disparity was calculated for every available perspective view using normalized cross correlation. This resulted in a large number of unique disparity maps, which were then combined in a least-squares sense, weighted by confidence coefficients to account for measurement uncertainty. The averaged disparity map was then transformed into a depth map using geometric optics. The accuracy of the algorithm was tested with a plenoptic camera on a small static target imaged throughout the depth of field. Depth was recovered with a standard deviation less than 5 mm, validating the algorithm.


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    Title :

    Correlation-Based Depth Estimation with a Plenoptic Camera


    Contributors:

    Published in:

    AIAA Journal ; 55 , 2 ; 435-445


    Publication date :

    2017-02-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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