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基于分形特征的红外图像识别方法
引用本文:李宏贵,李兴国,李国桢,罗正发.基于分形特征的红外图像识别方法[J].红外与激光工程,1999,28(1):20-24.
作者姓名:李宏贵  李兴国  李国桢  罗正发
作者单位:1. 南京理工大学电子工程系,南京,210094
2. 昆明物理研究所,昆明,650223
摘    要:文中首先对红外图像进行预处理,然后根据分形理论提取了红外图像的9种分形特征,用神经网络进行目标识别。这9种分形特征包括基于分形维数的特征,基于Hurst指数的分形特征和基于缝隙的分形特征。最后进行了计算机仿真实验,实验表明基于分形特征红外图像识别方法是可行的,并取得了较好的结果。

关 键 词:多重分形  多尺度分形  Hurst指数  神经网络  目标识别  红外图像

Infrared image recognition based on fractal feature
Li Honggui,Li Xingguo,Li Guozhen,Luo Zhengfa.Infrared image recognition based on fractal feature[J].Infrared and Laser Engineering,1999,28(1):20-24.
Authors:Li Honggui  Li Xingguo  Li Guozhen  Luo Zhengfa
Abstract:In this paper, firstly the infrared image was preprocessed, and 9 features were extracted from infrared image based on fractal theory, then neural network was used for target recognition. The 9 fractal features include the features based on fractal dimesion, the feature based on Hurst exponent and the feature based on lacunarity. The features based on fractal dimension include the fractal dimensions of original image, high gray value image, low gray value image, horizontal smoothing image, vertical smoothing image, mutil fractal dimension and mutil scale fractal dimension. Because the difference box counting algorithm and the algorithms of computing Hurst exponent and lacunarity feature are highly efficient, the velocity of feature extracting and recognition is quick enough. The computer simulation results show that the method based on fractal feature for infrared image recognition is available and gives better result.
Keywords:Multi fractal  Multi scale fractal  Hurst exponent  Neural network Target recognition  Infrared image  
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