【作者】 杨磊；

【导师】 赵明旺；

【作者基本信息】 武汉科技大学 ， 计算机应用技术， 2003， 硕士

【摘要】 基于笔迹的计算机身份鉴别是目前活跃于模式识别和图像处理领域的研究热点之一。小波分析具有出色的时间—频率域多分辨特性，十多年米在信息处理领域得到了迅速的发展。将小波分析引入到计算机笔迹鉴别中，探寻一些分布于不同频率域中的笔迹特征成为了计算机笔迹鉴别研究的一条新思路。 笔者将笔迹的书写过程理解为笔迹能量的空间分布过程。基于这一观点，笔者提出了一整套计算机笔迹纹理特征分析方法。首先，笔者提出了一种与传统方法截然不同的笔迹归一化办法，它即能够充分保留笔迹样本的空间分布信息，又可以有效的简化笔迹的预处理过程。接着，本文论述了一种与较常出现的小波基匹配降维方法有着本质区别的二维小波包最好基特征提取方法。该方法直接在二维空间上由db6小波包基对笔迹纹理实施3尺度小波包分解，再在由以香农熵为代价函数提取得到的15个小波包最好基处对分解系数实行重构。为了更好的描述这15个子纹理图像所包含的能量特征，本文提出了一种被称为非线性能量测度的子纹理图像能量特征值实现方法，实验证明这种办法具有纹理自适应匹配的能力。经过上述一系列处理后，一个汉字笔迹图像可以被压缩为一个含有15个元素的能量测度矩阵。将分解得到的各能量测度矩阵的组合经规范化后由BP神经网络进行学习和分类，实践证明此笔迹鉴别系统对实验中提取的有限样本的鉴别正确率可达95％以上。 本系统已由C++和Matlab混合编程实现。更多还原

【Abstract】 The computer writer identification based on handwriting is one of the research focuses in the field of Pattern Recognition and Image Processing. Because wavelet analysis has excellent characteristic in Timefrequency Multiresolution, it has been rapidly developed in the field of Information Processing in recent ten years. Applying wavelet analysis to computer handwriting identification and exploring some handwriting characteristic distributed in different frequency domains have become a new research idea of computer handwriting identification.The writing process is regarded as the distributing process of handwriting energy by author. According to this point of view, a set of characteristic analysis methods for computer handwriting texture are presented. Firstly, author presents a normalization method which is entirely different to tradititnal methods. The method can not only retain the spatial information of handwriting samples, but also efficiently simplify the preprocessing of them. Secondly, a 2-D Wavelet Packet best basis characteristic extraction method which is essentially different to the familiar basis matching method is described. The method directly executes wavelet packet decomposition of handwriting texture using wavelet packet basis db6 at scaling 3 in 2D space, then reconstructs the decomposition coefficient of 15 wavelet packet best basis which are took by Shannon Entropy Cost Function. In order to obtain the energy characteristic of texture sub-image, a nonlinear handwriting energy measure method is presented in this paper. It has been proved that the method has the adaptive capability to match the texture. After a series of above-mentioned processing, a Chinese character image can be compressed into an energy measure matrix which includes 15 elements. A BP Neural Networks is designed to learn and classify the result coming fom the combination and standardization of every energy measure matrix.The experiment proves that the recognition rate of the system can reach to 95% or more when the number of the experimental sample is limited.This system is implemented by mixed programing based on C++ and Matlab.更多还原