We introduce a novel stereo algorithm for computing a disparity map from a stereo image pair by minimizing a global cost function. The approach consists of two steps. First a "traditional" correlation-based similarity measurement is performed, then a relaxation takes place to eliminate possible ambiguities. The relaxation is formulated as a cost-optimizing approach, taking into account both the stereoscopic continuity constraint and considerations of the pixel similarity. The special formulation guarantees the existence of a unique minimum of the cost function which can be easily and rapidly found by standard numerical procedures. Results on real and synthetic images demonstrate the operative potential of the approach.


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    Title :

    Fast stereo vision for mobile robots by global minima of cost functions


    Contributors:
    Brockers, R. (author) / Hund, M. (author) / Mertsching, B. (author)


    Publication date :

    2004-01-01


    Size :

    346157 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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