叶志伟, 周欣, 郑肇葆, 赖旭东. 产生“Tuned”模板的的混沌粒子群算法[J]. 武汉大学学报 ( 信息科学版), 2013, 38(1): 10-14.
引用本文: 叶志伟, 周欣, 郑肇葆, 赖旭东. 产生“Tuned”模板的的混沌粒子群算法[J]. 武汉大学学报 ( 信息科学版), 2013, 38(1): 10-14.
YE Zhiwei, ZHOU Xin, ZHENG Zhaobao, LAI Xudong. Chaotic Particle Swarm Optimization Algorithm for Producing Texture “Tuned” Masks[J]. Geomatics and Information Science of Wuhan University, 2013, 38(1): 10-14.
Citation: YE Zhiwei, ZHOU Xin, ZHENG Zhaobao, LAI Xudong. Chaotic Particle Swarm Optimization Algorithm for Producing Texture “Tuned” Masks[J]. Geomatics and Information Science of Wuhan University, 2013, 38(1): 10-14.

产生“Tuned”模板的的混沌粒子群算法

Chaotic Particle Swarm Optimization Algorithm for Producing Texture “Tuned” Masks

  • 摘要: 针对传统的产生纹理"Tuned"模板方法的缺陷,在介绍混沌粒子群优化算法的基础上,提出了产生最佳"Tuned"模板的混沌粒子群方法;阐述了产生"Tuned"模板新方法的基本原理和实现步骤。通过对实际航空影像分类的实验表明,新方法对纹理影像的分类正确率令人满意。将混沌粒子群算法与基本粒子群算法的结果作了对比,结果表明,新方法具有较好的应用前景。

     

    Abstract: In order to overcome defects of traditional method for producing texture "Tuned" template this paper introduces the principle and steps of producing texture "Tuned" masks with chaotic particle swarm optimization algorithm;moreover,how to train "Tuned" masks with proposed method is illustrated in details.In the end,the proposed approaches have been implemented and tested on several real aerial images.Experiments results demonstrate that the proposed methods provide good search performance which are efficient methods to train "Tuned" masks;in addition,the performance of PSO and CPSO is compared,which indicates that the proposed method is promising in the practical application.

     

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