【摘要】 推导了基于Zernike矩的图像归一化标准,实验分析了数字图像离散化和噪声对Zernike矩计算的影响,并提出了相应的改进措施。最后,以BP神经网络作为分类器检验了改进的Zernike矩描述子的性能。试验结果显示改进的Zernike矩描述子能非常有效地保持平移、尺度、对比度和旋转不变性,并且能够有效抑制噪声影响。更多还原

【Abstract】 In this paper, the ZM-based image normalization criteria are derived, and it experimentally analyzes the accuracy of the ZM-based image normalization under the circumstance of the discretization of digital image and the presence of noise, and accordingly proposes several approaches to improve the accuracy of ZM-based image normalization. The BP neural network is utilized as the classifier to test the performance of the improved ZM descriptor.The experimental results show that the improved ZM descriptor can near-perfectly preserve the invariance of translation, scale, contrast and rotation, and can efficiently restrain the influence of noise.更多还原