@article {Otero2008, title = {Fuzzy-genetic optimization of the parameters of a low cost system for the optical measurement of several dimensions of vehicles}, journal = {Soft Computing}, volume = {12}, number = {8}, year = {2008}, month = {Jun}, pages = {751{\textendash}764}, abstract = {When designing optical measurement systems, it is common to use cameras, lenses and frame grabbers specially designed for metrology applications. These devices are expensive, therefore optical metrology is not the technology of choice in low cost applications. On the contrary to this, surveillance video cameras and home oriented frame grabbers are cheap, but imprecise. Their use introduces inaccuracies in the measurements, that sometimes can be compensated by software. Following this last approach, in this paper it is proposed to use fuzzy techniques to exploit the tolerance for imprecision of a practical metrology application (to automate the measurement of vehicle dimensions in Technical Inspection of Vehicles in Spain, the equivalent of the Ministry Of Transport Test or MOT Test in UK) and to find an economic solution. It will be shown that a genetic algorithm (GA), guided by a fuzzy characterization of the sources of error, can optimize the placement of the video cameras in a station so that these mentioned sensors can be used to take measurements within the required tolerance.}, issn = {1433-7479}, doi = {10.1007/s00500-007-0234-3}, url = {https://doi.org/10.1007/s00500-007-0234-3}, author = {Otero, J. and S{\'a}nchez, L. and Alcal{\'a}-Fdez, J.} } @article {Otero2003, title = {3D motion estimation of bubbles of gas in fluid glass, using an optical flow gradient technique extended to a third dimension}, journal = {Machine Vision and Applications}, volume = {14}, number = {3}, year = {2003}, month = {Jul}, pages = {185{\textendash}191}, abstract = {To solve the problem of estimating velocities of gas bubbles in melted glass, a method based on optical flow constraint (OFC) has been extended to the 3D case. A single camera, whose distance to the fluid is variable in time, is used to capture a sequence of frames at different depths. Since objects are not static, we cannot obtain two frames of different height values at the same time, and to our knowledge, this prevents the use of common 3D motion estimation techni ques. Since the information will be rather sparse, our estimation takes several measures around a given pixel and discards the erroneous ones, using a robust estimator. Along with the exposition of the practical application, the estimation proposed here is first contrasted in the 2D case to common benchmarks and then evaluated for a synthetic problem where velocities are known.}, issn = {1432-1769}, doi = {10.1007/s00138-002-0117-7}, url = {https://doi.org/10.1007/s00138-002-0117-7}, author = {Otero, J. and Otero, A. and S{\'a}nchez, L.} }