基于Bandelet变换能自适应跟踪图像几何方向的特点，将Bandelet变换引入到金属断口图像特征提取中，提出了一种基于Bandelet变换的金属断口形貌非线性识别方法。在提出的方法中，利用Bandelet变换提取金属断口图像的Bandelet熵作为特征向量，神经网络作为非线性分类器，对几种典型的金属断口图像进行了识别验证。同时，将该方法与基于传统的小波变换的金属断口图像识别方法进行了对比。结果表明，由于Bandelet变换克服了小波变换在处理金属断口图像时孤立对待边界各点的缺点，得到了比传统的小波变换方法更好的识别效果。

Based on the fact that Bandelet transform can adaptively track the geometric direction of the image, the transform is hence introduced to the feature extraction of metal fracture image and a new nonlinear identification method of metal fracture surface based on it is proposed. In the proposed method, the Bandelet entropy of metal fracture surface is extracted by the Bandelet transform and used as the feature vectors, and neural network as a nonlinear classifier. The proposed method is compared with the traditional recognition method of metal fracture based on wavelet transform. The experimental results show that the Bandelet transform overcomes the deficiency in the wavelet transform in dealing with the metal fracture edge, obtains more recognition effect.