Application of improved Hausdorff distance and quantum genetic algorithm in laser image guidance

ZHANG Tengfei; ZHANG Hexin; MENG Fei; QIANG Zhengjie; YANG Xiaogang

doi:10.7510/jgjs.issn.1001-3806.2016.03.004

In order to achieve high matching precision, good real-time performance and availability of target recognizing under shade condition in laser imaging guidance, a laser image matching algorithm was proposed based on improved Hausdorff distance and quantum genetic algorithm. In terms of the traditional Hausdorff algorithm and the problems of improving Hausdorff distance, the local edge feature of the image was selected as feature space. A new algorithm of improving Hausdorff distance was proposed to use it as a similarity measure. In the search strategy, the quantum genetic algorithm was chosen for parallel search. In order to prevent premature convergence of the population, the population catastrophe strategy was proposed and the speed and direction of convergence were adjusted by applying dynamic quantum rotation. Through theoretical analysis and experimental verification, target recognition contrast data under the condition of different parameters was obtained. The results show that the new algorithm, with good robustness, high matching precision and fast computing speed, could eliminate the effect of partial occlusion, noise and outlier.

Application of improved Hausdorff distance and quantum genetic algorithm in laser image guidance

Corresponding author: ZHANG Hexin, 541513539@qq.com;

1. Department of Control Engineering, Second Artillery Engineering University, Xi'an 710025, China

Received Date: 2015-04-01

Accepted Date: 2015-05-07

Available Online: 2016-05-25

Abstract: In order to achieve high matching precision, good real-time performance and availability of target recognizing under shade condition in laser imaging guidance, a laser image matching algorithm was proposed based on improved Hausdorff distance and quantum genetic algorithm. In terms of the traditional Hausdorff algorithm and the problems of improving Hausdorff distance, the local edge feature of the image was selected as feature space. A new algorithm of improving Hausdorff distance was proposed to use it as a similarity measure. In the search strategy, the quantum genetic algorithm was chosen for parallel search. In order to prevent premature convergence of the population, the population catastrophe strategy was proposed and the speed and direction of convergence were adjusted by applying dynamic quantum rotation. Through theoretical analysis and experimental verification, target recognition contrast data under the condition of different parameters was obtained. The results show that the new algorithm, with good robustness, high matching precision and fast computing speed, could eliminate the effect of partial occlusion, noise and outlier.

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Reference (16)

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Application of improved Hausdorff distance and quantum genetic algorithm in laser image guidance

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