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The Scientific World Journal
Volume 2013 (2013), Article ID 978548, 6 pages
Research Article

A Self-Adaptive Parameter Optimization Algorithm in a Real-Time Parallel Image Processing System

1State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 2, Yikuang Street, Harbin 150001, China
2School of Computer Science and Technology, Harbin Institute of Technology, No. 92, West DA-Zhi Street, Harbin 150001, China

Received 30 July 2013; Accepted 20 August 2013

Academic Editors: C.-C. Chang and F. Yu

Copyright © 2013 Ge Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Aiming at the stalemate that precision, speed, robustness, and other parameters constrain each other in the parallel processed vision servo system, this paper proposed an adaptive load capacity balance strategy on the servo parameters optimization algorithm (ALBPO) to improve the computing precision and to achieve high detection ratio while not reducing the servo circle. We use load capacity functions () to estimate the load for each processor and then make continuous self-adaptation towards a balanced status based on the fluctuated results; meanwhile, we pick up a proper set of target detection and location parameters according to the results of . Compared with current load balance algorithm, the algorithm proposed in this paper is proceeded under an unknown informed status about the maximum load and the current load of the processors, which means it has great extensibility. Simulation results showed that the ALBPO algorithm has great merits on load balance performance, realizing the optimization of QoS for each processor, fulfilling the balance requirements of servo circle, precision, and robustness of the parallel processed vision servo system.