This thesis is about digital watermarking technology and applications research based on fingerprint data . the main distributions of this thesis are : a fingerprint image watermarking algorithm in time / spatial domain is studied : most of traditional watermarks are pseudo - random sequences or binary images , which are of less information and worse security 本文是关于基于指纹数据的水印技术及其应用的一些研究,主要做了以下工作:研究了一种将指纹灰度图像作为水印信息的时间/空间域水印算法:传统水印信息大都是一维id序列(伪随机序列)或二值图像,这种水印技术所包含的信息量少,保密性差。
After reviewing some statistical models proposed to better characterize the host signal in dct , dwt and spatial domain , we describe the statistical property of host signal using gaussian scale mixture distribution , which is more general and flexible . we also present a new method for estimating the parameters of generalized gaussian distribution rapidly 研究了细节分量的统计模型,包括平稳高斯模型、平稳拉普拉斯模型、平稳广义高斯模型、局部平稳的高斯模型和高斯尺度混合模型,提出了一种广义高斯模型中形状参数的快速估计方法。
Tested on synthetic example , the results show that the spatial domain data processing methods presented in the thesis have higher resolution than the previous wave number domain methods . 2 . wavelet has been discussed , this is a new data processing method and the test on synthetic examples and field data show that it is a good latent method 通过数值实验本文认为,用本文空间域的解析延拓和导数的求取方法,在网格尺度相对于延拓高度很小的情况下,对磁异常进行延拓和求垂向导的精度是比较高的。