Singular points detection based on zero-pole model in fingerprint images

An algorithm is proposed which combines Zero-pole Model and Hough Transform(HT) to detect singular points. Orientation of singular points is defined on basis of Zero-pole Model which can further explain the practicability of Zero-pole Model. Contrary to orientation field generation, detection of singular points is simplified to determine the parameters of Zero-pole Model. HT uses rather global information of fingerprint images to detect singular points. This makes our algorithm more robust to noise than methods which only use local information. As Zero-pole Model may have a little warp from actual fingerprint orientation field, Poincare index is used to make position adjustment in neighborhood of the detected candidate singular points. Experimental results show that our algorithm performs well and fast enough for real time application in database NIST-4.     

指纹奇异点精确定位新方法

针对指纹图像奇异点快速精确定位的难题,提出一种简单实用算法。对指纹图像预处理,计算方向场并归域化,接着选出奇异点候选区,并以Poincare Index（PI）算法从中提取奇异点候选点集。对候选奇异点集去伪并精确定位。采用FVC2004指纹库进行实验验证,结果与PI算法对比,该算法鲁棒性更好,定位更精确,漏检率和误检率分别降低5.86%、6.8%,平均速度提高了3.71~9.38倍,基本满足高精度高速度的指纹奇异点定位要求。     

一种改进的指纹纹线方向场的计算方法

本文提出了一种改进的指纹纹线方向场计算方法，其中重点分析了奇异点模型中旋转角度的估计方法，并在复数域内简化了纹线方向场的优化计算．实验结果显示该方法充分利用了指纹纹线方向在全局上的渐变性和在奇异点附近的独特性，可以有效降低指纹图像中局部噪声的影响，提高指纹纹线方向场的计算精度，改进指纹图像的增强效果．     

一种鲁棒的指纹奇异点检测方法

提出一种两阶段的奇异点检测方法。将指纹图像分块，求出各块的方向构成块方向图，并在块方向图的基础上利用邻域方向的分布分析结合改进的Poincare Index方法来确定奇异点所在的候选区域，对候选区域中的像素再通过计算局部方向变化率来确定奇异点的精确位置。将此方法用于对FVC2004 DB1_A指纹数据库的图像，实验结果表明这种方法对指纹图像中的噪声有很好的鲁棒性，并且计算简单快速，易于实现。     

Modelling fingerprint ridge orientation using Legendre polynomials

The estimation of fingerprint ridge orientation is an essential step in every automatic fingerprint verification system. The importance of ridge orientation can be deflected from the fact that it is inevitably used for detecting, describing and matching fingerprint features such as minutiae and singular points. In this paper we propose a novel method for fingerprint ridge orientation modelling using Legendre polynomials. One of the main problems it addresses is smoothing orientation data while preserving details in high curvature areas, especially singular points. We show that singular points, which result in a discontinuous orientation field, can be modelled by the zero-poles of Legendre polynomials. The models parameters are obtained in a two staged optimization procedure. Another advantage of the proposed method is a very compact representation of the orientation field, using only 56 coefficients. We have carried out extensive experiments using a state-of-the-art fingerprint matcher and a singular point detector. Moreover, we compared the proposed method with other state-of-the-art fingerprint orientation estimation algorithms. We can report significant improvements in both singular point detection and matching rates.     

Pixel-level singular point detection from multi-scale Gaussian filtered orientation field

Singular point, as a global feature, plays an important role in fingerprint recognition. Inconsistent detection of singular points apparently gives an affect to fingerprint alignment, classification, and verification accuracy. This paper proposes a novel approach to pixel-level singular point detection from the orientation field obtained by multi-scale Gaussian filters. Initially, a robust pixel-level orientation field is estimated by a multi-scale averaging framework. Then, candidate singular points in pixel-level are extracted from the complex angular gradient plane derived directly from the pixel-level orientation field. The candidate singular points are finally validated via a cascade framework comprised of nested Poincare indices and local feature-based classification. Experimental results over the FVC 2000 DB2 confirm that the proposed method achieves robust and accurate orientation field estimation and consistent pixel-level singular point detection. The experimental results exhibit a low computational cost with better performance. Thus, the proposed method can be employed in real-time fingerprint recognition.     

High-speed maneuvering target detection approach based on joint RFT and keystone transform

Increasing the integration time is an effective method to improve small maneuvering target detection performance in radar applications.However,range migration and Doppler spread caused by maneuvering target motion during the integration time make it difficult to improve the coherent accumulation of target’s energy and detection performance.In this study,a new method based on Radon Fourier transform(RFT) and keystone transform(KT) for high-speed maneuvering target detection is proposed.The proposed algorithm utilizes second-order KT to correct the range curvature,and the improved dechirping method to compensate for the Doppler spread.RFT is then used to correct the range walk for target coherent detection.The method is capable of correcting the range migration and the time-varied Doppler frequency of the target without knowing its velocity and acceleration.The advantage of the proposed method is that it can increase the coherent integration time and improve detection performance under the condition of Doppler frequency ambiguity.Compared with the second-order RFT algorithm,the computational burden of the proposed method is greatly reduced under the premise that the two methods have similar estimation accuracy of range,velocity and acceleration.Numerical experiments demonstrate the validity of the proposed algorithm.     

Systematic methods for the computation of the directional fieldsand singular points of fingerprints

The first subject of the paper is the estimation of a high resolution directional field of fingerprints. Traditional methods are discussed and a method, based on principal component analysis, is proposed. The method not only computes the direction in any pixel location, but its coherence as well. It is proven that this method provides exactly the same results as the "averaged square-gradient method" that is known from literature. Undoubtedly, the existence of a completely different equivalent solution increases the insight into the problem's nature. The second subject of the paper is singular point detection. A very efficient algorithm is proposed that extracts singular points from the high-resolution directional field. The algorithm is based on the Poincare index and provides a consistent binary decision that is not based on postprocessing steps like applying a threshold on a continuous resemblance measure for singular points. Furthermore, a method is presented to estimate the orientation of the extracted singular points. The accuracy of the methods is illustrated by experiments on a live-scanned fingerprint database     

一种用于指纹方向场估计的网格插值模型

指纹方向场的估计是指纹识别预处理算法中的重要环节,对算法识别效率起到关键作用。本文提出了一种网格插值模型,该模型以指纹奇异点为中心,将指纹平面做网格划分,利用插值算法建立了方向场与指纹奇异点之间的非线性关系。模型中利用了指纹的全局信息来调整网格点的值,使得它与传统的基于局部信息的方向场算法有本质的区别。在FVC2002 和FVC2004 指纹数据库上的实验结果表明,该模型比传统算法具有更高的准确性和鲁棒性,同时对于低质量的指纹图像,仍然能够给出很好的方向场估计。     

基于奇异点的字典模型指纹方向场去噪方法*

方向场估计是指纹识别过程中非常重要的步骤。传统方法如基于梯度的方法等在处理潜指纹图像时很容易受噪音干扰,而最近提出的基于字典模型的方法无法解决“真词错误”的问题。针对上述问题,本文提出一种融合了零极点模型的字典模型的指纹方向场去噪方法,即将指纹方向场看做是零极点控制的方向场和平滑的残差方向场相叠加的结果,通过首先用零极点模型生成正确的零极点控制的方向场,然后用字典模型修正残差方向场方向场,最后将零极点模型生成的方向场与去噪后的残差方向场融合形成重建方向场,通过基于奇异点的字典模型,我们解决了“真词错误”的问题。为了验证算法的有效性,在NIST SD27潜指纹图像数据库上进行了实验。实验结果表明：对于潜指纹,本文算法能获得比字典模型更精确的方向场,继而可以更好地增强潜指纹图像,并在后续的匹配实验中取得更好的结果。     

基于Gaussian-Hermite矩的指纹奇异点定位

在指纹分类和识别算法中,提取的奇异点(core点和delta点)数目和奇异点的准确位置是非常重要的.介绍了一种基于Gaussian-Hermite矩分布属性的自适应指纹奇异点定位方法,为了准确地确定奇异点,用到了指纹图像在多种尺度下的不同阶Gaussian-Hermite矩分布,并用一种基于主分量分析(principal component analysis,简称PCA)的方法分析指纹图像的Gaussian-Hermite矩分布.实验结果表明,该算法能够准确地确定奇异点位置.     

基于复数滤波的指纹奇异点提取方法

在身份识别中,指纹具有唯一性,人的指纹包含大量信息,识别指纹图像很重要.针对指纹图像奇异点中准确判断和精确定位的难题,提出了一种改进的指纹奇异点提取方法.首先,对指纹平方复数点方向场进行多尺度滤波,在平滑噪声的同时,保留奇异区方向的细节信息.对方向场进行复数滤波,并运用启发式规则增强复数滤波的响应幅度,通过分析响应的幅度检测奇异点的位置,由响应的相位确定方向.在FVC 2000上进行实验.结果表明,上述方法可精确提取指纹图像中各奇异点的位置及其方向信息,证明优于与其它方法,结果具有较好的鲁棒性.     

基于连续分布方向图的指纹奇异点检测

在指纹连续分布方向图(场)的基础上,对经典的PoincaréIndex计算公式进行了改进,提出了一种新的基于连续分布方向图的指纹奇异点检测算法。由于指纹连续分布方向图过渡平滑、自然,既具有很好的连续性、渐变性和抗噪性,又具有较高的精确度;而改进后的PoincaréIndex不仅能精确表示向量场的旋转角度,而且还能精确表示向量场的旋转方向。所以,该算法能够在像素级水平精确定位指纹奇异点(core point和delta point),精确度达到一个像素。在FVC2000、FVC2002和FVC2004的训练指纹库(Set B)以及笔者采集的AFIS2004指纹库(含4000幅指纹)上的实验结果验证了该算法的有效性。     

Efficient linear solution of exterior orientation

This paper concerns an efficient algorithm for the solution of the exterior orientation problem. Orthogonal decompositions are used to first isolate the unknown depths of feature points in the camera reference frame, allowing the problem to be reduced to an absolute orientation with scale problem, which is solved using the singular value decomposition (SVD). The key feature of this approach is the low computational cost compared to existing approaches     

一种对奇异值不敏感的ISOMAP

ISOMAP是一种经典的非线性降维方法，能够有效地发现高维非线性数据集的低维几何结构，但该算法对奇异值和噪声非常敏感。利用具有鲁棒性的主成分分析（Robust PCA）来探测奇异点，并对奇异点进行适当处理以降低ISOMAP对其的敏感程度。所提出的算法直观且易于理解，实验结果也证明它具有较好的鲁棒性，而且在奇异点较多的情况下仍能保持数据的整体结构。     

在图像处理中对指纹识别的研究

该文从介绍指纹识别技术的原理和指纹识别算法入手,将指纹识别中分类和匹配过程相结合,提出了一种包含奇异点周边的方向场和细节点等特征的奇异点邻近结构。该结构利用奇异点周边识别信息集中的特点,大大减少了匹配的计算量,并能够同时作为指纹分类和比对的特征,直接应用于指纹的连续分类和快速匹配过程,实现对大容量指纹数据库的快速识别。该算法在保证自动识别指纹系统的识别准确性的同时,还使得指纹在线识别系统的1:N辨别速度有明显的提高。     

Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix

This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet (PST) of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a novel information criterion (NIC), in which the stationary points are singular triplet of the crosscorrelation matrix. Then, based on Newton's method, we obtain a coupled system of ordinary differential equations (ODEs) from the NIC. The ODEs have the same equilibria as the gradient of NIC, however, only the first PST of the system is stable (which is also the desired solution), and all others are (unstable) saddle points. Based on the system, we finally obtain a fast and stable algorithm for PST extraction. The proposed algorithm can solve the speed-stability problem that plagues most noncoupled learning rules. Moreover, the proposed algorithm can also be used to extract multiple PSTs effectively by using sequential method.      

基于短距离优先加权均值的Stoilov改进算法

在使用均值滤波算法修复Stoilov相移算法中出现的奇异点时,会损失相位的细节信息,从而导致计算出的相位存在误差.针对这一问题,提出一种基于短距离优先选择原则的加权均值修正算法.首先,采用统计逼近的原则标记出奇异点;其次,采用短距离优先原则为每个奇异点构造最近邻滤波窗口,窗口的范围依非奇异点个数和当前所能取得的最短距离的情况而定;最后,用窗口中满足要求的非奇异点的加权平均值代替奇异点,实现对奇异点的修正.仿真与实验结果表明,该方法使窗口划分更加细致,能有效去除脉冲噪声,在相位分布细节处理上更具优势,且使得误差均方根低于0.06cm.     

基于立体视觉的串联机器人跟踪检测系统

建立一种六自由度串联机器人视觉跟踪检测系统框架,包括图像采集、摄像机标定、机器臂跟踪检测、机器臂位姿建模与计算等。提出利用CamShift算法对机器人进行在线粗跟踪,搜寻和画定出机器臂操作器在当前窗口的区域位置。对跟踪到的机器臂按照SURF算法进行特征提取与立体匹配。该方法被用于对串联机器人位姿检测进行实验。实验结果表明,2种算法的结合适用于六自由度串联机器人在空间复杂运动的跟踪检测。     

Singular points detection based on multi-resolution in fingerprint images

The singular points of fingerprints, namely core and delta, play an important role in fingerprint recognition and classification systems. Several traditional methods have been proposed; however, these methods cannot achieve the reliable and accurate detection of poor-quality fingerprints. In this paper, an algorithm is proposed which combines improved Poincaré index and multi-resolution analysis to detect singular points. Conventional Poincaré index method is improved on the basis of the Zero-pole Model analysis to detect singular points with different resolutions. A model is presented to extract the multi-resolution information of the fingerprint pattern; this model divides fingerprint image into nonoverlapping blocks corresponding to different block sizes on the basis of wavelet functions to compute multiple resolution directional fields, and block position shifting is performed on these resolution levels to capture the features of the ridge direction patterns, where the corresponding shifting intervals are based on Sampling theorem. The relationship of singularities detected by improved Poincaré index in different resolution directional fields is used to confirm singular points accurately and reliably. The combination of local and global information makes our algorithm more robust to noise than methods that use local information only, and the existence of this algorithm increases the insight into the nature of singular points extraction. The accuracy and reliability of the method are demonstrated by experiment on database NIST-4, public fingerprint databases FVC02 DB1 and DB2.