欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
分形理论在图像的纹理识别中得到了广泛应用,由于分形维数不能反映图像的空间信息,容易造成误识别。针对该问题并结合声纳图像的特点,通过提升结构构造了Haar小波,并将提升小波变换同分形理论相结合,利用小波分解的多分辨率特点和分形维数的多尺度特性,提高图像的识别率。采用Levenberg-Marquardt（L-M）算法优化的BP神经网络对不同信噪比的声纳图像进行分类识别。实验结果表明,文中方法不论在识别率还是识别时间上均优于传统纹理识别方法。
英文摘要：
Fractal dimension has been widely used in the recognition of the texture images, but it lacks the ability to describe spatial information of images. In Considering the characteristic of a sonar image, the paper uses the lifting scheme to construct the Haar wavelet, and relates the lifting scheme with fractal d- imension. Amalgamation of multi-scales characteristics of wavelet; transform and fractal dimension increased the recognition rate. LMBP neural network is used to recognize the sonar images of different SNR. The results show that the new method has a higher classification rate and is more efficient than traditional methods.
同期刊论文项目
同项目期刊论文
期刊信息
位置：