基于体全息的光电混合虹膜识别系统的研究

对基于体全息的光电混合虹膜识别系统进行了研究,采用基于联合最优小波包基的特征图像相关识别方法实现虹膜的光学识别。生成联合最优小波包基的特征图像以压缩存储的模板图像库,降低后处理数据量。改进最优小波包基优选标准,使所选最优基获得最大识别能力。采用统计特征识别的后处理方法使光电混合识别系统具有更好的适应性。系统仿真结果表明,所提出的系统既具有光学运算和体全息相关的高并行性,又具有计算机数值计算的高精度,获得了良好的识别效果。     

基于多尺度分析的小波不变矩

本文结合小波多尺度分析和不变矩的优点,通过坐标变换和几何规范化等预处理,提出了一种能提供多尺度分析且具有平移、尺度和旋转不变性的矩特征描述子-小波不变矩,并给出了其不变性证明.讨论了不同类型和阶数的小波函数对小波不变矩识别性能的影响.实验结果表明,由合适消失矩阶数的、对称紧支集双正交小波函数得到的小波不变矩具有最佳的分类特性.     

基于联合变换相关的光学小波实时图像边缘增强

提出了一种基于联合变换相关的光学小波实时图像边缘增强的方法．选用Mexican—hat小波，利用小波函数的多分辨率特性，用联合变换相关的方法实现了缩放后的小波母函数与待识别图像函数的互相关(即边缘增强图像)，得到了较满意的实验结果．     

基于小波结构矩的图像识别算法

提出了一种基于小波结构矩的具有平移、旋转、缩放不变性的新型图像识别方法.小波结构矩是在小波矩的基础上通过改变图像函数的结构即几何矩的密度得到的.该算法结合了小波和结构矩的优点,不但实现了对图像特征的精细把握还增大了相似图像之间的距离.采用三次样条函数作为母小波,有效提取了图像的全局特征和局部特征.实验证明,小波结构矩比改进的Hu矩和结构矩具有更高的识别率,目前该算法已经成功运用到全自动金丝球焊机图像识别系统.     

基于联合变换相关的光学小波实时图像边缘增强

提出了一种基于联合变换相关的光学小波实时图像边缘增强的方法.选用Mexican-hat小波,利用小波函数的多分辨率特性,用联合变换相关的方法实现了缩放后的小波母函数与待识别图像函数的互相关(即边缘增强图像),得到了较满意的实验结果.     

小波变换在目标识别中的应用

利用小波变换具有易于消除噪声、运算方便、能够体现图像特征等优点,研究了基于小波变换的目标识别方法.首先采用二次B样条小波滤波器组对样本图像进行小波变换,提取多尺度边缘,然后提取不变矩,以此作为图像的特征向量,最后应用小波神经网络进行分类识别.计算机仿真实验表明,该方法取得了较好的识别效果.     

基于相关分析的飞机目标识别方法

提出了一种基于相关分析的飞机目标识别方法。该方法利用飞机图像低频和高频部分合成滤波器模板,能达到很高识别率与很低的等错率。该研究旨在提高飞机识别的准确率和降低出错率,采用一种基于相关分析的飞机目标识别方法。该方法通过对采集的飞机图像做去除背景、降噪、图像增强、二值化和归一化处理,将飞机图像低频和高频部分合成滤波器模板,通过特征比对达到识别飞机的目的。利用Matlab 7.0做10种飞机的识别实验,得出了95.47%识别率和0.04%等错率的结论,识别率和等错率均优于不变矩法、三维识别方法、基于小波分析和矩不变量的方法,印证了笔者提出的基于相关分析的飞机目标识别方法的优越性。在飞机图像数据库上的实验结果表明,该方法是可行的。     

航空图像压缩的双正交小波滤波器整数化设计

在航空图像压缩中,通常采用具有线性相位、正则性、消失矩和完全重构,及适于硬件实现、实时等特性的小波。根据小波滤波器设计,提出了一种基于图像压缩的构造整数双正交小波滤波器的设计方法。从选择小波基的原则为出发点,以CDF9-7小波基为参考,以压缩效果为准则来构造出更优的双正交整数小波基,并且采用航空图像为标准训练图像,以压缩比、峰值信噪比、压缩后保留能量百分比为参数,来寻找最优的小波基。试验结果证明,此方法可以实施非常简单的、无浮点乘法的运算,因而减少运算复杂性以及降低小波硬件实现的难度。     

多母小波自适应小波滤波器

利用自适应小波变换（AWT）能融合不同母小波的优点,采用盖伯母小波和墨西哥帽母小波构成自适应小波.以光学人脸识别中降噪问题为应用背景,使用神经网络法对小波参数和组合系数进行优化,将生成的多母小波自适应小波滤波器用作人脸特征提取器.对噪声图像做特征提取,进行相关识别,采用3个指标定量分析识别结果.同盖伯小波和墨西哥帽小波识别结果的比较表明,多母小波自适应小波具有不同母小波的优点,并有良好的降噪性能.     

用小波变换对ECG信号进行去噪研究

小波消失矩阶数的不同,对应的小波滤波器的幅频曲线也不相同,因此选用不同的小波滤波器对信号进行滤波,去噪效果会有明显差异。本文通过数学建模研究分析小波滤波器的幅频特性,明确小波幅频特征及与小波滤波器消失矩的阶数之间的关系,为选择最优小波滤波器提供理论依据。本文提出针对ECG噪声的频率特点实现精确陷波去噪,有效的保留了信号的奇异点与特征值,减少了信号失真。实验结果表明,选择具有相对最优消失矩阶数的提升小波滤波器对ECG进行去噪处理,可以使信号能量分布更加集中,去噪效果更好。      

一种快速自适应最优小波包基搜索算法

在小波包调制系统中,小波包基的选取对于系统性能有着显著的影响。针对目前BBS算法与BFA算法的缺点,分析了小波包基的构成原则与选取方法,提出了一种基于根节点逐级向下按二进分支路分解,利用度量函数决定父节点与子节点取舍的最优基搜索算法,并解决了约束条件下的最优基输出问题。最后将该算法在4径Rayleigh信道模型下进行了测试,仿真结果表明：在相同条件下,该算法比BBS算法快15%~20%,比BFA算法快10~15倍,且输出的最优基比全分解结构的ICI与ISI联合功率降低约20%。     

最佳小波包多载波调制解调技术

本文利用离散小波包变换（ＤＷＰＴ）导出信号窨的诱导小波包基。在此基础上建立起基于ＤＷＰＴ的小波包多载波传输系统的概念,提出了最佳小波包多载波市制解调技术。技术根据给定的信道特性,采用类似于最佳基搜索城众多的小波包多载波市制解调方案中快速寻求出最佳方案,使得系统解调出的信号中所要信号与符号间干扰的信干比在不增强噪声的前提下达了最大。     

用二元小波滤波器作图像相关识别

本文提出用二元小波滤波器作图像相关识别的技术。利用小波变换对图像处理的优越性,对小波滤波函数进行二元化处理,成为二元带通滤波器,并应用于旋转图像的识别,本文进行了理论阐述和计算机模拟实验,结果证实了设计的可行性,与经典的联合变换相关器相比输出功能大为提高。     

On the Dual-Tree Complex Wavelet Packet and $M$-Band Transforms

The two-band discrete wavelet transform (DWT) provides an octave-band analysis in the frequency domain, but this might not be ldquooptimalrdquo for a given signal. The discrete wavelet packet transform (DWPT) provides a dictionary of bases over which one can search for an optimal representation (without constraining the analysis to an octave-band one) for the signal at hand. However, it is well known that both the DWT and the DWPT are shift-varying. Also, when these transforms are extended to 2-D and higher dimensions using tensor products, they do not provide a geometrically oriented analysis. The dual-tree complex wavelet transform , introduced by Kingsbury, is approximately shift-invariant and provides directional analysis in 2-D and higher dimensions. In this paper, we propose a method to implement a dual-tree complex wavelet packet transform , extending the as the DWPT extends the DWT. To find the best complex wavelet packet frame for a given signal, we adapt the basis selection algorithm by Coifman and Wickerhauser, providing a solution to the basis selection problem for the . Lastly, we show how to extend the two-band to an -band (provided that ) using the same method.     

Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection

We propose a novel scheme for signal compression based on the discrete wavelet packet transform (DWPT) decompositon. The mother wavelet and the basis of wavelet packets were optimized and the wavelet coefficients were encoded with a modified version of the embedded zerotree algorithm. This signal dependant compression scheme was designed by a two-step process. The first (internal optimization) was the best basis selection that was performed for a given mother wavelet. For this purpose, three additive cost functions were applied and compared. The second (external optimization) was the selection of the mother wavelet based on the minimal distortion of the decoded signal given a fixed compression ratio. The mother wavelet was parameterized in the multiresolution analysis framework by the scaling filter, which is sufficient to define the entire decomposition in the orthogonal case. The method was tested on two sets of ten electromyographic (EMG) and ten electrocardiographic (ECG) signals that were compressed with compression ratios in the range of 50%-90%. For 90% compression ratio of EMG (ECG) signals, the percent residual difference after compression decreased from (mean +/- SD) 48.6 +/- 9.9% (21.5 +/- 8.4%) with discrete wavelet transform (DWT) using the wavelet leading to poorest performance to 28.4 +/- 3.0% (6.7 +/- 1.9%) with DWPT, with optimal basis selection and wavelet optimization. In conclusion, best basis selection and optimization of the mother wavelet through parameterization led to substantial improvement of performance in signal compression with respect to DWT and randon selection of the mother wavelet. The method provides an adaptive approach for optimal signal representation for compression and can thus be applied to any type of biomedical signal.     

实现小波变换图像识别的光电混合实验系统

本文依据联合变换相关器,设计一套实现小波变换图像识别的光电混合实验系统,联合变换功率谱及相关输出强度分布分别用CCD采集并输入计算机进行处理,证明小波变换可以明显改善光学图像相关识别能力;同时,该系统提高了光学相关识别实验的灵活性和可操作性。     

虹膜识别中的采集系统研究

介绍了虹膜识别系统中的识别方法及相应的采集系统设计；系统设计中采用新型CMOS成像芯片成像、光学低通滤波器滤波和自对准、红外照明、防Xuan光等无侵犯性设计；文章详细介绍了相关器件及系统参数设计，并给出运用此系统所进行的的虹膜识别对比实验结果。该系统设计具有一定的参考价值。     

基于小波包变换和特征选择的雷达辐射源信号识别

为了提高雷达辐射源信号的正确识别率以满足现代电子对抗的需要，提出一种基于小波包变换和特征选择的雷达辐射源信号识别新方法。先采用小波包变换进行特征提取，再采用基于量子遗化算法的相像系数特征选择法来挑选出小波包特征中分辨能力强的特征。仿真实验结果显示，该方法用较少的特征能获得较高的正确识别率，具有一定的参考价值。     

Complexity-constrained best-basis wavelet packet algorithm forimage compression

The concept of adapted waveform analysis using a best-basis selection out of a predefined library of wavelet packet (WP) bases allows an efficient image representation for the purpose of compression. Image coding methods based on the best-basis WP representation have shown significant coding gains for some image classes compared with methods using a fixed dyadic structured wavelet basis, at the expense however, of considerably higher computational complexity. A modification of the best-basis method, the so-called complexity constrained best-basis algorithm (CCBB), is proposed which parameterises the complexity gap between the fast (standard) wavelet transform and the best wavelet packet basis of a maximal WP library. This new approach allows a `suboptimal' best basis to be found with respect to a given budget of computational complexity or, in other words, it offers an instrument to control the trade-off between compression speed and, coding efficiency. Experimental results are presented for image coding applications showing a highly nonlinear relationship between the rate-distortion performance and the computational complexity in such a way that a relatively small increase in complexity with respect to the standard wavelet basis results in a relatively high rate distortion gain