改进的Gabor滤波算法在指纹识别中的应用

指纹自动识别系统中图像的处理和匹配算法在系统的设计中起了尤为重要的作用,本文对Gabor滤波算法进行了简化,同时引入了HGF（Half Gabor Filter）对指纹图像进行了增强处理。实验结果表明经过这种改进的Gabor滤波算法提高了图像的处理速度,并很好的保留纹线的关键信息。     

基于Gabor滤波器的指纹图像快速增强

研究并实现了利用Gabor滤波器对指纹图像增强的算法。改进了指纹图像方向图和纹线频率的提取方法,同时也对Gabor滤波器的快速实现方法进行研究,首先把Gabor滤波器分解为多个不同方向上有着不同参数的一维高斯滤波器的组合,然后通过递归的方法分别实现这些高斯滤波器,最后实现了Gabor滤波器的快速算法在指纹图像增强中的应用。实践表明,该方法效果良好,速度快,能大幅度提高指纹图像质量。     

基于Gabor小波核心算法的指纹图像预处理

本文介绍了一种基于小波技术的指纹图像增强算法——Gabor滤波。详细的阐述了Gabor滤波器的构造及其在指纹图像增强中的应用，并从原理上分析了应用中遇到的问题，提出了改进的方法。     

基于Gabor函数的小波域指纹图像增强算法

针对指纹大规模采集库中存在的指纹图像局部区过干或过湿的问题，提出了一种基于Gabor函数的小波域指纹增强算法。该算法在小波域利用低频系数图估计指纹方向，从而抑制了指纹局部过干或过湿的影响，进而分别实现基于Gabor函数的小波域各子图增强，最终将各增强子图利用小波逆变换实现重构。通过对FVC2004的DB1指纹库中的部分低质量图像的增强结果比较，该算法对低质量指纹图像的增强效果明显，且处理速度明显快于现存的Gabor增强方法。     

改进参数的Gabor滤波器指纹图像增强算法的研究

在指纹识别之前,首先要对指纹图像进行预处理操作,预处理中,Gabor滤波器能够很好的平滑和分割指纹图像,然而由于Gabor滤波器对指纹中纹路的走向和频率非常的敏感,导致经过Gabor滤波操作后的指纹图像特征点剧烈的缺失和变化。该文利用相位差二值化,使其在指纹图像二值化时能够不需要Gabor滤波,然而对指纹增加时通过Gabor,使得在增强过程中有效的连接纹理的断裂并且很好的平滑图像,这样做使得Gabor滤波对纹路频率的敏感不再影响指纹的预处理,并且很好的保留了指纹的特征。     

基于Radon变换的指纹图像滤波增强方法

基于Gabor滤波实现过干指纹图像的增强是一种较好的方法,如何估计Gabor滤波器的参数是解决问题的关键。本文采用Radon变换来估计Gabor滤波中的方向和频率,然后进行Gabor滤波增强。通过对FVC2000指纹库中的部分过干的指纹图像进行增强,结果表明,该方法增强效果明显,处理速度较快。     

基于Gabor滤波器的指纹图像增强算法

介绍了一种基于Gabor滤波器的指纹图像增强算法,该方法对传统的Gabor滤波器的参数和大小进行了优化。实验表明这种算法具有很好的处理效果。     

基于Gabor滤波的指纹增强算法

介绍了一种改进的Gabor滤波的指纹图像增强算法。提出一种新的指纹图像分割算法，并对指纹核心区域进行多方向滤波合成。实验证明，该套增强算法运行稳定，效果好，鲁棒性强。     

一种指纹图像增强算法研究

指纹增强技术可以有效地加强指纹的脊线特征,为指纹细节的提取和匹配奠定可靠的基础.提出一种基于Gabor滤波和形态学方法相结舍的指纹图像增强算法,进行指纹图像的规格化处理,从8个不同的方向进行Gabor滤波并重构,利用形态学方法进行细化处理输出纹理清晰的指纹图像,取得较好效果.     

基于二值指纹图像的Gabor滤波增强算法

在指纹预处理过程中,Gabor滤波器对于图像的平滑和分割具有十分显著的效果。但是Gabor滤波器对纹线方向和频率十分敏感,通过处理灰度图像得到的二值图像存在着严重的特征点丢失和变化问题。采用相位差分二值化方法,使二值化过程不依赖Gabor滤波。提出了在二值图像上利用Gabor滤波器增强的方法,保留了Gabor滤波器连接纹线断线和滤除图像中噪声的优点,避免了Gabor滤波器对频率的敏感性问题,有效地提高了指纹特征的保持度。     

Removal of digitization errors in fingerprint ridgelines using B-splines

Most of the contemporary automatic fingerprint identification systems (AFIS) are based on a dual strategy of combining the minutiae information with the ridge topography in order to improve the overall matching performance. To ensure the efficiency and robustness of such an AFIS, it is necessary, therefore, to rectify the abnormalities or aberrations of the underlying ridge topography, in general, and to smoothen the uneven/noisy ridgelines, in particular. The proposed work deals with one such problem besetting fingerprint analysis—the problem of eliminating digitization errors that usually creep in during fingerprint acquisition or during preprocessing. The method mainly involves fitting of B-splines for a set of control points chosen appropriately for each ridgeline in a fingerprint image. These fitted splines, in turn, can be used to reconstruct the concerned fingerprint, which, after the rectification procedure, becomes almost devoid of such digitization error. With a proper “smoothness parameter” that determines the extent to which a ridgeline is smoothed, the structural information of the corrected ridgelines produces improved results on fingerprint matching. Experimental results on several databases have been reported, which clearly demonstrate the strength and elegance of the proposed algorithm.     

A new model for fingerprint classification by ridge distribution sequences

In this paper, a new method is introduced which is a combination of structural and syntactic approaches for fingerprint classification. The goal of the proposed ridge distribution (R-D) model is to present the idea of the possibility for classifying a fingerprint into the complete seven classes in the Henry's classification. From our observation, there exist only 10 basic ridge patterns which construct fingerprints. Fingerprint classes can be interpreted as a combination of these 10 ridge patterns with different ridge distribution sequences. In this paper, the classification task is performed depending on the global distribution of the 10 basic ridge patterns by analyzing the ridge shapes and the sequence of ridges distribution. The regular expression for each class is formulated and a NFA model is constructed accordingly. An explicit rejection criterion is also defined in this paper. For the seven-class fingerprint classification problem, our method can achieve the classification accuracy of 93.4% with 5.1% rejection rate. For the five-class problem, the accuracy rate of 94.8% is achieved. Experimental results reveal the feasibility and validity of the proposed approach in fingerprint classification.     

基于改进的Gabor滤波指纹图像增强算法研究

对Gabor滤波器进行改进,改进的算法利用了指纹图像的局部特性,结合局部四邻城的关联特性,并且采用固定频率来代替复杂的频率计算.实验结果证明,改进的Gabor滤波算法不仅增强了指纹图像信息,同时也提高了处理速度.     

一种新的基于距离的汉字笔画抽取方法

在定义了相关术语的基础上提出了一种新的基于距离的汉字笔画抽取方法，并给出了实验结果，结果表明该方法可有效可行，为后续基于笔画的汉字信息处理打下了坚实的基础。     

岭回归法用于五种酚的同时测定

将岭参数引入最小二乘法中,提出了一种新的计算分光光度法－岭回归分光光度法。将该法应用于具紫外吸收的五组分酚类化合物的同时测定。文中详细介绍了该法的基本原理,岭参数以及介质的选择,讨论了波长测定点数及混合标准溶液份数对计算结果的影响。     

Reduced Rank Kernel Ridge Regression

Ridge regression is a classical statistical technique that attempts to address the bias-variance trade-off in the design of linear regression models. A reformulation of ridge regression in dual variables permits a non-linear form of ridge regression via the well-known kernel trick. Unfortunately, unlike support vector regression models, the resulting kernel expansion is typically fully dense. In this paper, we introduce a reduced rank kernel ridge regression (RRKRR) algorithm, capable of generating an optimally sparse kernel expansion that is functionally identical to that resulting from conventional kernel ridge regression (KRR). The proposed method is demonstrated to out-perform an alternative sparse kernel ridge regression algorithm on the Motorcycle and Boston Housing benchmarks.     

基于骨架的血细胞图像分离算法

文章从细胞图像骨架的角度出发,研究和发展了一种新的粘连细胞分离算法。其主要思路是:首先采用细化算法提取细胞图像的骨架,并计算骨架各点的边界距离值,然后利用骨架边界距离函数波谷的特性将相互粘结的细胞分离。新算法提出了一种计算骨架边界距离值的新方法用于分析骨架波谷的特性,并在理论和实践的基础上,研究和发展了细胞图像中粘结细胞的分离准则。为证明该算法的有效性,选取了上百种不同的细胞图像进行了测试。实验结果表明,新算法能有效、快速地对细胞图像中相互粘结的细胞进行合理分割。     

Finding ridges and valleys in a discrete surface using a modified MLS approximation

Implicit surface fitting is a promising approach to finding ridges and valleys in discrete surfaces, but existing techniques are time-consuming and rely on user-supplied tuning parameters. We use a modified MLS (moving-least-squares) approximation technique to estimate the local differential information near a vertex by means of an approximating surface. Ridge and valley vertices are easily detected as zero-crossings, and can then be connected along the direction of principal curvature. Our method, demonstrated on several large meshed models, produces a good fit which leads to improved visualization. It does not oscillate and is quick to compute.     

Finding ridges and valleys in a discrete surface using a modified MLS approximation

Implicit surface fitting is a promising approach to finding ridges and valleys in discrete surfaces, but existing techniques are time-consuming and rely on user-supplied tuning parameters. We use a modified MLS (moving-least-squares) approximation technique to estimate the local differential information near a vertex by means of an approximating surface. Ridge and valley vertices are easily detected as zero-crossings, and can then be connected along the direction of principal curvature. Our method, demonstrated on several large meshed models, produces a good fit which leads to improved visualization. It does not oscillate and is quick to compute.     

一种面向指纹识别的鲁棒脊线跟踪算法

由于传统的指纹识别系统大多要进行二值化和细化过程将消耗大量的计算时间,提出了一种基于脊线跟踪的指纹图像特征点提取算法.在灰度级指纹图像上,沿脊线方向自适应跟踪指纹脊线,直至该条脊线终止或与其他脊线相交,得到一幅细化后的指纹骨架图和附在其上的细节点信息.跟踪过程中,在关键点处进行脊线方向估计和局部滤波,跳跃式地获得脊线骨架点.对于提取到的末端点和交叉点,根据指纹图像的结构特征和统计结果相结合进行去伪后处理.实验证明算法的有效性.