利用小波分解和分形维数进行声纳图像识别

以分形维数和多重分形的概念为基础，通过计算原图像和灰度差分图像的分形维数和多重分形维数，形成了纹理特征集的第一部分；然后对声纳图像采用树式结构小波变换，将小波变换各个频带输出的熵作为纹理分类的特征，并根据特征本身的离散程度对其进行规范化处理，构成了纹理特征集的第二部分；最后将这两部分组合，对不同信噪比的声纳图像进行分类识别．识别结果表明，文中方法的识别率可达到90％以上．

Sonar Image Recognition by Wavelet Decomposition and Fractal Dimension

Firstly, we employ a technique based on the knowledge of fractal dimension (FD) and multi-fractal dimension (MFD) The FD and MFD features are extracted from the original image and the differential gray images Then a tree-structured wavelet transform is employed to extract the entropy for each frequency channel of the wavelet transform output as the features for texture classification and the features are normalized according to their own degree of dispersion The features of both fractal and wavelet are then integrated to fulfill the recognition task Recognition experiments have been made on the sonar images in different signal-to-noise ratios and the test results show that a recognition ratio greater than 90% can be achieved by the approach