基于曲波变换的渐变镜头转场检测算法

曲波变换在处理“曲线奇异”时有明显的优势。给出了曲波变换的计算方法、公式和具体步骤。对dissolve渐变转场过程中的各相邻帧进行曲波变换。以曲波变换后高频系数的平方作为图像帧的特征量,计算相邻帧间此特征量的欧氏距离。用大量的高清码流对算法进行了实验。实验结果表明,此算法有较高的查全率和查准率。     

基于自适应脊波变换的边缘检测

通过分析现有的一些方法,提出了一种基于自适应脊波变换的边缘检测方法。这种方法以脊波变换为理论基础,具有多方向和多尺度性,能对图像中的不同方向的边缘特征进行有效的表示和检测。实验表明,对于边缘主要表现为直线而其他位置光滑的图像,该检测方法抗噪声更强,定位更准确。     

基于Haar小波变换和分块DCT的人脸识别

提出一种新的基于二维离散小波分解和分块离散余弦变换的降维方法.该方法与模式识别领域中用于特征提取和降维的PCA-LDA方法进行了比较.结果表明,此方法与PCA-LDA方法在识别率上大体相当,但它比其更具有计算量小、降维速度快的优点.因此,该方法对于人脸识别是一种有效降维手段.     

基于小波变换和正交质心算法的人耳识别研究*

提出了一种新的快速对人耳图像进行特征提取的方法,先对人耳图像进行二维的离散小波分解,然后使用正交质心算法对小波分解后得到的低频信息进行降维,进而获得图像的特征向量。实验证明,该方法与模式识别领域中广泛应用的Fisherfaces方法相比,在识别率大体相当的前提下,具有计算量小、降维速度快的优点,是对人耳图像进行特征提取的一种有效手段。     

Fingerprint verification based on minutiae features: a review

Fingerprints have been an invaluable tool for law enforcement and forensics for over a century, motivating research into automated fingerprint-based identification in the early 1960s. More recently, fingerprints have found an application in biometric systems. Biometrics is the automatic identification of an individual based on physiological or behavioural characteristics. Due to its security-related applications and the current world political climate, biometrics is presently the subject of intense research by private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive research that has been done on automated fingerprint matching over the last four decades. In particular, the focus is on minutiae-based algorithms. Minutiae features contain most of a fingerprints individuality, and are consequently the most important fingerprint feature for verification systems. Minutiae extraction, matching algorithms, and verification performance are discussed in detail, with open problems and future directions identified.

基于Haar小波的多尺度NLOS误差抑制算法

针对无线定位中非视距（Non-Line of Sight,NLOS）误差对定位精度的影响,在分析NLOS误差特性的基础之上提出了多尺度误差抑制算法。该算法将信号的多尺度估计方法和卡尔曼滤波相结合,利用小波变换特有的低通滤波特性能和小波阀值去噪能够很好地消除到达时间/到达时间差分（Time of Arrival/Time Diff of Arrival,TOA/TDOA）测量值中的NLOS误差,给出了Haar小波的实现方法。仿真实验结果表明,该算法在不同的NLOS误差模型和不同的信道环境下均能很好地抑制NLOS误差,较大幅度地提高了定位精度。     

A multi-matcher for ear authentication

In this work we propose a local approach of 2D ear authentication. A multi-matcher system is proposed where each matcher is trained using features extracted from a single sub-window of the whole 2D image. The features are extracted by the convolution of each sub-window with a bank of Gabor Filters, then their dimensionality is reduced by Laplacian EigenMaps. The best matchers, corresponding to the most discriminative sub-windows, are selected by running the Sequential Forward Floating Selection (SFFS). Our experiments, carried out on a database of 114 people, show that combining only few (ten) sub-windows in the fusion step it is possible to achieve a very low Equal Error Rate.     

基于小波变换和脊波变换的自适应图像去噪算法

为了克服单纯小波变换或脊波变换的不足,提出了基于小波变换和脊波变换的自适应去噪算法。实验结果表明,在处理点奇异性和线奇异性的图像时,该方法比单纯小波变换或脊波变换的阈值去噪算法更具优越性,在实际应用中更为有效。     

A finger-vein verification system using mean curvature

The finger-vein pattern is one of the human biometric signatures that can be used for personal verification. The first task of a verification process using finger-vein patterns is extracting the pattern from an infrared finger image. As a robust extraction method, we propose the mean curvature method, which views the vein image as a geometric shape and finds the valley-like structures with negative mean curvatures. When the matched pixel ratio is used in matching vein patterns, experimental results show that, while maintaining low complexity, the proposed method achieves 0.25% equal error rate, which is significantly lower than what existing methods can achieve.     

Personal identification based on finger-vein features

Finger-vein recognition refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals. The advantages of finger vein over traditional biometrics (e.g. face, fingerprint, and iris) lie in low-risk forgery, noninvasiveness, and noncontact. This paper here presents a new method of personal identification based on finger-vein recognition. First, a stable region representing finger-vein network is cropped from the image plane of an imaging sensor. A bank of Gabor filters is then used to exploit the finger-vein characteristics at different orientations and scales. Based on the filtered image, both local and global finger-vein features are extracted to construct a finger-vein code (FVCode). Finally, finger-vein recognition is implemented using the cosine similarity measure classifier, and a fusion scheme in decision level is adopted to improve the reliability of identification. Experimental results show that the proposed method exhibit an exciting performance in personal identification.     

一种新的脊波变换方法

小波变换适用于表示具有各向同性奇异性对象的局部特性,脊波变换适用于表示具有各向异性奇异性对象的局部特性,但是各自对于对方所适用的局部特性的应用效果却不明显.提出了一种新的对脊波理论加以改进的多分辨分析方法——拟脊波多分辨分析方法.该方法统一了小波理论和脊波理论,使小波理论和脊波理论成为该方法的两种特殊情形.同时它具有对各向同性和异性的奇异性对象的辨识能力.通过实验比较表明,该方法对小波理论和脊波理论优点的组合、缺点的规避相当明显,在图像去噪应用中具有更强的灵活性.     

基于分块形态Haar小波变换的盲水印算法

提出了一种基于形态Haar小波变换的数字水印算法。该算法对载体图像按分块进行形态Haar小波变换,水印的嵌入和提取在子块的形态小波域中完成,每个子块嵌入水印的一个像素信息。该算法能实现水印的快速盲提取。实验证明,与分块DCT法和分块DWT法相比,算法不仅简单快速,而且大大提高了水印的不可觉察性和鲁棒性,在抵抗噪声攻击方面更有优势。     

基于图像统计特征的小波变换插值方法

针对一般图像插值方法常见的两个缺点:边缘锯齿、细节模糊,提出了一种基于图像统计特征的小波变换插值方法,该方法利用图像插值前后边缘统计特征的相似性,从低分辨率图像中获取估算高分辨率图像所需的协方差矩阵和向量,保持了边缘光滑性。同时利用小波分解与重构,将图像非边缘的细节特征进行了增强。该方法无需迭代运算,计算量较小。实验结果表明了用该方法获取高分辨率图像的有效性和可行性。     

基于多核计算平台和高速缓存感知的Haar小波变换算法

针对目前多核计算平台的普及性以及多核平台的强大计算能力,通过充分利用高速缓存的工作原理以及多线程程序设计的优势,提出了一种在多核平台上高速缓存优化的并行Haar小波计算算法.通过测定算法运行过程中高速缓存缺失率以及算法在不同多核计算平台上的运算性能,反映了该算法极大地降低了缺失率和减少了计算时间.如在数据规模8192条件下缺失率从95%降低到8.37%,计算时间从4.35s减至0.89s.由此证明了该计算方法具有高速且可移植的特性.     

A machine-vision system for iris recognition

This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endevour stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement. In particular, it is known in the biomedical community that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remote examination with the aid of a machine-vision system. The body of this paper details the design and operation of such a system. Also presented are the results of an empirical study in which the system exhibits flawless performance in the evaluation of 520 iris images.     

一种基于分类器投票的车牌定位方法

为解决类似车牌和失真车牌的定位难题,提出一种基于分类器投票的车牌定位方法。方法从两个方面提升车牌定位精度:首先,针对类似车牌和失真车牌的图像特点,提出两种新的车牌图像描述子,针对性地提升两类车牌的定位效果;其次,使用多种描述子分别训练SVM分类器,采用分类器投票融合的方式决定最终分类结果,进一步提升定位准确度。实验结果表明:(1)相比传统的小波和LBP车牌图像描述子,所提算法有效地提高了失真车牌的定位精度,降低了类似车牌的识别错误率。(2)构建的投票融合分类器方法使车牌图像的分类错误率从单个描述子最优的3.05%下降到了0.8%。     

基于二进脊波变换的图像去噪

二进小波变换在每次分解时不进行下采样,与小波级数相比,它是冗余的,且二进小波变换的部分系数扰动不会带来重构信号的严重失真。在相同的误判概率下,基于二进小波变换的信号去噪效果会好于基于小波级数变换的信号去噪效果。基于这个思想,该文提出了一种基于二进脊波变换的图像去噪算法。实验结果证明,与小波级数相比,该算法具有更好的去噪效果。     

基于组合特征和BTSVM的电能质量扰动识别

为了克服单一特征不能完全表征各种暂态扰动信号特征的不足,提出了一种基于组合特征和二叉树结构支持向量机相结合的电能质量多分类方案。利用小波包变换对扰动信号进行分解,提取特定频带下信号的能量,利用S变换获得扰动信号的模矩阵,从中提取出特征信息,然后将多频带信号的能量和对应的S变换特征信息组合得到组合特征。对依据聚类思想设计出的二叉树结构支持向量机分类器进行了训练和测试。仿真结果表明,该方法具有较好的准确性和识别速度,能够有效识别常见扰动信号,平均识别率提高了6%以上,测试总用时缩短0.06秒,训练时间减小1.8秒。     

基于不同小波的CWOFDM系统的仿真与分析

DWT比DFT在子信道间有更高的频谱约束力,可以去除COFDM(编码正交频分复用)系统中必须的CP(循环前缀),从而提高带宽利用率。对不同小波下CWOFDM(编码小波正交频分复用)系统在无线信道下的传输性能进行仿真。结果表明,CWOFDM系统抗噪声和多径衰落性能优于COFDM,而且小波的滤波器长度和分解层数都会影响CWOFDM系统抗噪声和多径衰落的性能。     

基于边缘统计特征的遥感图像融合改进方法

针对传统小波变换融合方法中未对高频分量作进一步分解从而忽略了细节信息的缺点,提出了一种基于区域边缘特征的小波包融合算法。该方法对小波包分解后的低频分量采用能量加权的融合规则,高频分量则利用各个子带的方向性,计算其边缘特征统计量,通过权值法得到融合系数。对SPOT多光谱图像和高分辨率图像进行融合实验,实验数据和理论分析表明,该方法的平均梯度等评价参数均有提高,在保持光谱信息的同时,有效地改进了图像的空间特征信息。