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Abstract

This paper presents a 3D distance measurement accuracy improvement for stereo vision systems using optimization methods A Stereo Vision system is developed and tested to identify common uncertainty sources. As the optimization methods are used to train a neural network, the resulting equation can be implemented in real time stereo vision systems. Computational experiments and a comparative analysis are conducted to identify a training function with a minimal error performance for such method. The offered method provides a general purpose modelling technique, attending diverse problems that affect stereo vision systems. Finally, the proposed method is applied in the developed stereo vision system and a statistical analysis is performed to validate the obtained improvements.

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Authors and Affiliations

J.C. Rodríguez-Quiñonez
O. Sergiyenko
W. Flores-Fuentes
M. Rivas-lopez
D. Hernandez-Balbuena
R. Rascón
P. Mercorelli

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