一种基于群体智能的自动目标检测后处理算法

Chen Zhuo, Liu Xiangshuang, Zhu Xiaodong
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引用次数: 4

摘要

目前,大多数目标检测后处理算法缺乏自适应能力,这使得它们在从数字图像中自动检测目标方面存在许多局限性。为此,本文提出了一种新型的自动目标检测后处理算法(CASI),该算法采用基于群体智能的聚类算法对检测到的目标像素进行后处理。CASI具有自组织、鲁棒性、可扩展性和简单性等特点,能够自适应地恢复目标像素,将目标像素划分为不同的目标,并删除虚警目标。实验结果表明,该算法的目标检测后处理效果明显优于常用的形态学变换算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Automatic Target Detection Post-Processing Algorithm Based on Swarm Intelligence
Presently, most target detection post-processing algorithms are short of self-adapting, which make them have many limitations in automatic target detection from the digital images. Therefore this paper provides a new type of automatic target detection post-processing algorithm (CASI) which adopts clustering algorithm based on swarm intelligence to post-process the detected target pixels. Because of the characteristics of self-organization, robustness, expandability and simplicity, CASI can self-adaptively restore the target pixels, divide them into different targets and delete the false alarm targets. Experimental results conform that the target detection post-processing effect of this new type of algorithm is much better than the ones of morphological transformation in common use.
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