基于细节点与方向场信息的指纹残缺区域重建算法

 由于指纹的残缺区域缺失有效信息,使得通过细节点和脊线信息来进行指纹匹配的工作难以进行.本文针对指纹残缺区域中脊线断裂和细节点缺失的现象,提出了一种重建算法.融合邻域中的细节点和方向场信息对残缺区域的方向场进行估计;然后基于估计出的方向场并结合先验知识来分析残缺部分的脊线与细节点分布情况,对其进行修复;最后,引入信息熵对多种重建方案进行评估,从而确定残缺部分的最佳重建结果.实验结果表明,该算法能够对面积较大并可能包含细节点的残缺区域进行较好的重建,进而提高后续匹配过程的准确率.     

基于小波变换的指纹全局方向场重建简

从大型指纹库识别不完整或局部指纹仍然是今天的一大挑战。在此文中,我们研究利用小波变换用于指纹方向场重建问题。特别的,我们提出了一种基于小波的多尺度方法从局部的指纹重建全局拓扑表达。然后提出基于结合参数模型和非参数模型来描述重建问题,我们提供了一般表达式可用于所有有效的反变换模型的解决方案。这种方案允许我们分割保护存在的方向场同时预测丢失的未知部分的结构。我们还开发了基于脊线拓扑特征的一些先验信息来估计丢失的方向结构的算法。我们的统计实验表明我们提出的基于模型的算法能有效的恢复指纹的方向场并应用于指纹匹配,这样重大的提高了局部指纹识别其他模块性能。     

基于小波变换的指纹全局方向场重建

从大型指纹库识别不完整或局部指纹仍然是今天的一大挑战。在此文中,我们研究利用小波变换用于指纹方向场重建问题。特别的,我们提出了一种基于小波的多尺度方法从局部的指纹重建全局拓扑表达。然后提出基于结合参数模型和非参数模型来描述重建问题,我们提供了一般表达式可用于所有有效的反变换模型的解决方案。这种方案允许我们分割保护存在的方向场同时预测丢失的未知部分的结构。我们还开发了基于脊线拓扑特征的一些先验信息来估计丢失的方向结构的算法。我们的统计实验表明我们提出的基于模型的算法能有效的恢复指纹的方向场并应用于指纹匹配,这样重大的提高了局部指纹识别其他模块性能。     

采用改进梯度互信息和粒子群优化算法的红外与可见光图像配准算法

针对传统互信息图像配准容易产生局部极值,以及传统梯度互信息配准方法计算量大等问题,在互信息和梯度方法基础上构建了一种改进的梯度互信息方法,该方法直接统计梯度图像的互信息,有效地将图像梯度信息和灰度信息结合起来,不仅保证了配准精度,而且较传统梯度互信息方法减少了计算量。在参量优化的过程中,针对传统粒子群优化算法易陷入局部极值的缺点,提出了改进的粒子群优化算法,该算法在传统粒子群优化算法基础上引入混沌优化思想和遗传算法中的杂交思想,不仅能够有效抑制局部极值,而且加快了收敛速度。多种红外与可见光图像配准实验结果证明,文中提出的算法能够有效提高配准精度和速度。     

基于细节点和全局方向信息的混合指纹匹配算法

本文研究了一种指纹细节点与全局方向信息在分类器级别上融合的混合指纹匹配算法。其中细节点的匹配利用了点模式匹配的思想算法。此外研究并设计了全局方向匹配算法，并将两者融合。试验结果表明了算法的有效性。     

方向相关与互信息加权组合多模图像配准方法

针对可见光与红外图像的配准问题,提出了一种基于边缘方向相关和局部优选区域互信息加权的配准方法。首先进行区域划分,建立区域优选测度,选取信息量丰富的局部区域;在优选区域的基础上,引入了图像的边缘方向信息,构造边缘方向图,以增加全局空间特征;然后,通过综合局部区域灰度信息和全局边缘方向信息,将互相关测度和互信息测度加权集成,构造了一种新的相似测度。实验结果表明,边缘方向相关测度的引入,提高了基于互信息的图像配准的精度,利用局部优选区域代替整幅图像提高了算法的速度。     

一种基于模式熵的残缺指纹识别算法

该文提出了一种新的残缺指纹识别算法,在应用融合特征的同时,利用模式熵进行相似性度量。由于残缺指纹的特有性质,识别结果主要由两方面决定,即提取包含足够信息的特征以及有效的相似性度量方式。对于第1个问题,该文将细节点和方向场特征进行有效融合,来得到更全面的信息,并提高尺度和旋转不变性。对于第2个问题,通过引入模式熵度量两个特征点集之间的一致性,并以此来消除误匹配。在指纹库中进行的大量实验以及同其他方法的充分比较表明,该文提出的算法在准确率和速度上都取得了较优的性能。     

基于进化策略的多传感器检测系统的优化设计

本文提出一种聂曼-皮尔逊准则下分布式多传感器检测系统优化设计的混合优化算法,该算法以进化策略方法进行全局搜索,辅以Powell方向加速法进行局部搜索,无须使用目标函数的导数信息.对分布式恒虚警检测系统的优化设计结果表明,混合优化算法在收敛性和精度上都优于梯度算法.     

基于特征融合的指纹分割算法

指纹图像的分割是指纹识别系统中的重要环节之一，分割算法的优劣直接影响指纹自动识别系统的性能，因此指纹分割技术的研究有着重要的意义。一幅指纹图像，在指纹区灰度变化剧烈，在垂直于指纹局部方向上有着稳定的周期性。现对梯度和指纹周期设定阈值，提出了一种融合梯度和周期两种特征的分割算法。在梯度的计算上改用旋转不变的Sobel算子；在局部周期的计算上，通过对傅里叶变换的分析推理，提出了一种新的周期计算方法。分析、处理结果都表明：该算法抗噪能力强、分割效果好，具有很高的实用价值，能够满足一般指纹识别系统性能要求。     

低质量指纹图像的自适应滤波

提出了基于BEG算法和小波交换的方法用以增强图像的质量．首先提出了局部图像的梯度估计方法,称之为BEG(梯度的最佳估计)算法,用来提取图像的纹理方向特征参数,然后。在指纹的局部窗内计算其频率参数．最后,根据这些参数,用Morlet小波滤波器对指纹图像进行自适应滤波．性能测试结果表明,该算法在实际中具有较高的应用价值．     

Fingerprint recognition by combining global structure and local cues.

As an important feature, orientation field describes the global structure of fingerprints. It provides robust discriminatory information other than traditional widely-used minutiae points. However, there are few works explicitly incorporating this information into fingerprint matching stage, partly due to the difficulty of saving the orientation field in the feature template. In this paper, we propose a novel representation for fingerprints which includes both minutiae and model-based orientation field. Then, fingerprint matching can be done by combining the decisions of the matchers based on the global structure (orientation field) and the local cue (minutiae). We have conducted a set of experiments on large-scale databases and made thorough comparisons with the state-of-the-arts. Extensive experimental results show that combining these local and global discriminative information can largely improve the performance. The proposed system is more robust and accurate than conventional minutiae-based methods, and also better than the previous works which implicitly incorporate the orientation information. In this system, the feature template takes less than 420 bytes, and the feature extraction and matching procedures can be done in about 0.30 s. We also show that the global orientation field is beneficial to the alignment of the fingerprints which are either incomplete or poor-qualitied.     

Fingerprint registration by maximization of mutual information.

Fingerprint registration is a critical step in fingerprint matching. Although a variety of registration alignment algorithms have been proposed, accurate fingerprint registration remains an unresolved problem. We propose a new algorithm for fingerprint registration using orientation field. This algorithm finds the correct alignment by maximization of mutual information between features extracted from orientation fields of template and input fingerprint images. Orientation field, representing the flow of ridges, is a relatively stable global feature of fingerprint images. This method uses the statistics and distribution of global feature of fingerprint images so that it is robust to image quality and local changes in images. The primary characteristic of this method is that it uses this stable global feature to align fingerprints, and that its behavior may resemble the way humans compare fingerprints. Experimental results show that the occurrence of misalignment is dramatically reduced and that registration accuracy is greatly improved at the same time, leading to enhanced matching performance.     

Incomplete fingerprint recognition based on feature fusion and pattern entropy

Considering the inherent characteristics of incomplete fingerprint： local feature loss and global information distortion, the recognition progress has been mainly restricted by two critical problems： how to precisely extract informative features and still with compact representation of the incomplete fingerprint; and how to effectively measure the similarity between fingerprint images. In this paper, to handle the first problem, both the minutiae and orientation field feature are extracted and then fused to get a more comprehensive feature with scale and rotation invariability. Dealing with the second one, the pattern entropy is introduced to robustly measure the similarity of two incomplete fingerprints. Extensive experiments have been conducted on both those popular fingerprint databases and our extended databases containing more incomplete fingerprints. Meanwhile, thorough performance comparisons have been made with existing approaches. Experimental results show that our approach has more efficient ability especially in incomplete fingerprint recognition, and also performs well in both accuracy and efficiency.     

Image registration by maximization of combined mutual information and gradient information

Mutual information has developed into an accurate measure for rigid and affine monomodality and multimodality image registration. The robustness of the measure is questionable, however. A possible reason for this is the absence of spatial information in the measure. The present paper proposes to include spatial information by combining mutual information with a term based on the image gradient of the images to be registered. The gradient term not only seeks to align locations of high gradient magnitude, but also aims for a similar orientation of the gradients at these locations. Results of combining both standard mutual information as well as a normalized measure are presented for rigid registration of three-dimensional clinical images [magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET)]. The results indicate that the combined measures yield a better registration function does mutual information or normalized mutual information per se. The registration functions are less sensitive to low sampling resolution, do not contain incorrect global maxima that are sometimes found in the mutual information function, and interpolation-induced local minima can be reduced. These characteristics yield the promise of more robust registration measures. The accuracy of the combined measures is similar to that of mutual information-based methods.     

A model-based method for the computation of fingerprints'' orientation field

As a global feature of fingerprints, the orientation field is very important for automatic fingerprint recognition. Many algorithms have been proposed for orientation field estimation, but their results are unsatisfactory, especially for poor quality fingerprint images. In this paper, a model-based method for the computation of orientation field is proposed. First a combination model is established for the representation of the orientation field by conidering its smoothness except for several singular points, in which a polynomial model is used to describe the orientation field globally and a point-charge model is taken to improve the accuracy locally at each singular point. When the coarse field is computed by using the gradient-based algorithm, a further result can be gained by using the model for a weighted approximation. Due to the global approximation, this model-based orientation field estimation algorithm has a robust performance on different fingerprint images. A further experiment shows that the performance of a whole fingerprint recognition system can be improved by applying this algorithm instead of previous orientation estimation methods.     

基于互信息量和模糊梯度相似性的医学图像配准

本文分析了基于互信息量的医学图像配准算法中存在的鲁棒性问题,提出创建图像的模糊梯度场及建立模糊梯度相似性测度,并将其结合到互信息量配准算法当中.实验证明,本方法很好的解决了传统基于互信息量的方法中存在的鲁棒性问题,能够快速稳定地实现医学图像配准.     

Reconstructing Orientation Field From Fingerprint Minutiae to Improve Minutiae-Matching Accuracy

Minutiae are very important features for fingerprint representation, and most practical fingerprint recognition systems only store the minutiae template in the database for further usage. The conventional methods to utilize minutiae information are treating it as a point set and finding the matched points from different minutiae sets. In this paper, we propose a novel algorithm to use minutiae for fingerprint recognition, in which the fingerprint's orientation field is reconstructed from minutiae and further utilized in the matching stage to enhance the system's performance. First, we produce ldquovirtualrdquo minutiae by using interpolation in the sparse area, and then use an orientation model to reconstruct the orientation field from all ldquorealrdquo and ldquovirtualrdquo minutiae. A decision fusion scheme is used to combine the reconstructed orientation field matching with conventional minutiae-based matching. Since orientation field is an important global feature of fingerprints, the proposed method can obtain better results than conventional methods. Experimental results illustrate its effectiveness.