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Classical and Neural Models for Binocular Stereoscopic Reconstruction

publicado a la‎(s)‎ 20 nov. 2015 12:37 por JEAN PIERRE DIAZ PAZ   [ actualizado el 20 nov. 2015 12:38 ]

Autores:

    Jean Pierre Díaz Paz
    Humberto Loaiza Correa

We present a comparative study about stereoscopic reconstruction process focused in modelling for parallel axis stereoscopic cameras. We used two classical models and one based on Artificial Neural Networks for modelling the parallel axis system. Then we used the root mean square of distances between the point coordinates calculated from images and measured from the calibration pattern to evaluate the accuracy of each model. We compared the accuracy of two classical models and one based on Artificial Neural Networks. By comparing the confidence interval for every obtained model we observed that the classical model of Silven and Heikkila [1] shows average errors of 1.0 cm, however this error was reduced to 0.4 cm by an adjustment proposed in this paper. On the other hand the neural networks showed less robust to the training set. The current work can be extended to future developments in areas like photogrammetry, architecture and robotics.

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