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Proceedings Paper

Optical flow optimization using parallel genetic algorithm
Author(s): Olmo Zavala-Romero; Guillermo Botella; Anke Meyer-Bäse; Uwe Meyer Base
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Paper Abstract

A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.

Paper Details

Date Published: 4 June 2011
PDF: 13 pages
Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80581E (4 June 2011); doi: 10.1117/12.883680
Show Author Affiliations
Olmo Zavala-Romero, The Florida State Univ. (United States)
Guillermo Botella, The Florida State Univ. (United States)
Anke Meyer-Bäse, The Florida State Univ. (United States)
Uwe Meyer Base, The Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 8058:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
Harold Szu, Editor(s)

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