采用小波变换的立体匹配:一种基于相位的方法

本义提出了一种采用小波变换基于相位的立体匹配算法。该算法引入多分辨率分析思想,借助小波变换的Ｍａｌｌａｔ算法,采用金字塔式的多尺度匹配结构,从而显著地提高了匹配效率。同时,该算法利用平滑性约束条件实现了相位不稳定区域视差的自适应插值,保证了良好的匹配效果。立体象对的测试结果表明了该算法的有效性。     

一种基于小波变换的多分辨率门限选取方法

本文描 一种亲的门限选择方法，它基于小流变换多尺度方法分析图像直方图的信息，通过对小波变换后直方图零交叉点检测，得到一序列门限，用于描述图像的边缘特征，整个方法采用一种由粗到细引导的多分辨率门限结构。     

基于小波零交叉基元的多分辨率图像匹配方法

文中将小波变换运用到立体视觉的图像匹配中,选取合适的二进小波基,以二进小波变换的多尺度零交叉为匹配基元,较多地利用了图像的结构特征。在匹配过程中采用由粗到精的层决匹配策略和严格的匹配约束,减小了对应点搜索范围,并从局部和整体两个角度进行匹配后的质量控制。实验结果证明,该方法具有较高的匹配精确度和较快的匹配速度,并且算法稳定。     

基于小波变换的图像融合技术研究

基于小波变换的融合算法形成了多分辨率融合算法的一个新的有用框架。由于人类视觉只对亮度信号的局部对比度敏感,提出了小波对比度的概念。图像经小波多分辨率分解后,依照小波对比度,定义变换系数邻域活性和系数匹配程度测度,按照系数匹配程度加权系数形成融合图像。将小波多分辨率分解和人类视觉对局部对比度敏感的特性优雅地结合在一起。     

基于小波分析的多余物三维定位算法

本文详细论述了基于小波变换立体匹配技术的多余物三维定位算法，提出以二进小波变换的系数作为匹配基元，匹配算法从匹配基元局部属性的相似性和全局匹配的相容性两个方面解决模糊匹配的问题。实验结果表明该算法稳定可靠，效果较好。     

基于方向性小波子树分形图像编码的改进算法

本文在分析混合分形零树小波图像编码算法(FZW)优缺点的基础上，提出一种新的基于方向性小波子树的分形图像编码算法。该算法结合零树小波编码和分形编码，通过在匹配搜索过程中使用方向性range和domain子树，提高匹配精度，改善了传统分形小波图像压缩中的方块效应，更大限度的保留了图像的边缘信息。实验结果表明，该算法在提高压缩比和去除图像的方块效应方面，均取得了良好的效果。     

基于超混沌序列的信息隐藏算法

利用混沌对初值敏感性和小波变换多分辨率特性,提出了一种新的安全性信息隐藏方法。该算法首先利用Lyapunov指数大的4阶Chebyshev混沌映射及具有复杂相空间结构的耦合映像格子(CML)来构造安全的超混沌序列密码流,然后利用此超混沌序列将秘密信息嵌入到图像的小波域,最后进行逆小波变换生成隐秘图像。算法提取秘密信息时不需要原始图像。实验结果表明,该隐藏算法具有很好的透明性、鲁棒性和强的安全性。     

基于二进小波变换的虹膜识别算法研究

讨论了虹膜图像定位等预处理方法，并提出了基于二进小波变换过零检测的虹膜图像识别算法，利用图像在不同分辨率下的特征表示，对虹膜上的采样同心圆计算不同分辨率下的过零小波变换，产生1－D信号，然后对它用相异度函数与模板特征比较。实验表明，该方法克服了漂移、旋转和比例放缩带来的局限，并且识别率高，效果良好。     

基于小波分解的塔式快速图像匹配算法

文章利用小波分析的多分辨率特性，构建了小波金字塔上的快速图像匹配算法。首先对低分辨率的图像进行匹配操作，然后逐级上推，最终实现全分辨率下的图像匹配。实验结果表明该算法可以减少计算量、显著提高匹配效率。     

使用小波变换的图象边缘检测算法

将小波变换理论应用于图象边缘提取，提出了一种新的图象边缘检测算法。一种相对于（ｃ０，ｃ３）＝（０．０５，０．０５）的对称尺度函数被用于二进小波变换，得到原图象的一个多分辨率表达式，再提取图象的边缘特征。     

使用小波变换的图象边缘检测算法

将小波变换理论应用于图象边缘提取,提出了一种新的图象边缘检测算法。一种相对于(c0,c3)=(0.05,0.05)的对称尺度函数被用于二进小波变换,得到原图象的一个多分辨率表达式,再提取图象的边缘特征。实验结果表明,本方法是解决图象边缘提取的一个十分有效的模型。     

Zero-crossings of a wavelet transform

The completeness, stability, and application to pattern recognition of a multiscale representation based on zero-crossings is discussed. An alternative projection algorithm is described that reconstructs a signal from a zero-crossing representation, which is stabilized by keeping the value of the wavelet transform integral between each pair of consecutive zero-crossings. The reconstruction algorithm has a fast convergence and each iteration requires O( N log² (N)) computation for a signal of N samples. The zero-crossings of a wavelet transform define a representation which is particularly well adapted for solving pattern recognition problems. As an example, the implementation and results of a coarse-to-fine stereo-matching algorithm are described     

Multiscale video representation using multiresolution motioncompensation and wavelet decomposition

A multiscale video representation using wavelet decomposition and variable-block-size multiresolution motion estimation (MRME) is presented. The multiresolution/multifrequency nature of the discrete wavelet transform makes it an ideal tool for representing video sources with different resolutions and scan formats. The proposed variable-block-size MRME scheme utilizes motion correlation among different scaled subbands and adapts to their importance at different layers. The algorithm is well suited for interframe HDTV coding applications and facilitates conversions and interactions between different video coding standards. Four scenarios for the proposed motion-compensated coding schemes are compared. A pel-recursive motion estimation scheme is implemented in a multiresolution form. The proposed approach appears suitable for the broadcast environment where various standards may coexist simultaneously     

基于Haar小波的地形匹配技术

在传统相关地形匹配的基础上，提出了一种基于Haar小波的多尺度地形匹配方法。这种方法用Haar小波对地形作多尺度分解，然后，利用分解后的HL和LH分量分别进行相关匹配，对匹配后的结果用Dempster-Shafer证据理论进行融合，得到正确匹配结果。由于Haar小波分解后减少了数据量，保留了原始地形的主要几何特征，所以，它的计算速度和匹配精度都优于传统的匹配方法。     

Fast multiresolution motion estimation algorithms for wavelet-based scalable video coding

Motion estimation and compensation in wavelet domain have received much attention recently. To overcome the inefficiency of motion estimation in critically sampled wavelet domain, the low-band-shift (LBS) method and the complete-to-overcomplete discrete wavelet transform (CODWT) method are proposed for motion estimation in shift-invariant wavelet domain. However, a major disadvantage of these methods is the computational complexity. Although the CODWT method has reduced the computational complexity by skipping the inverse wavelet transform and making the direct link between the critically sampled subbands and the shift-invariant subbands, the full search algorithm (FSA) increases it. In this paper, we proposed two fast multiresolution motion estimation algorithms in shift-invariant wavelet domain: one is the wavelet matching error characteristic based partial distortion search (WMEC-PDS) algorithm, which improves computational efficiency of conventional partial distortion search algorithms while keeping the same estimate accuracy as the FSA; another is the anisotropic double cross search (ADCS) algorithm using multiresolution-spatio-temporal context, which provides a significantly computational load reduction while only introducing negligible distortion compared with the FSA. Due to the multiresolution nature, both the proposed approaches can be applied to wavelet-based scalable video coding. Experimental results show the superiority of the proposed fast motion estimation algorithms against other fast algorithms in terms of speed-up and quality.     

基于小波变换的加权最小二乘相位解缠算法

提出了一种求解加权最小二乘相位解缠问题的新算法,采用相位导数方差相关图定义了相位数据的初始权重,给出了利用多分辨率小波变换进行相位解缠的具体实现方法.利用离散小波变换,将原线性系统转化成具有较好收敛条件的等价新系统,通过独立求解新系统中的低频部分,加快了系统的收敛速度.仿真实验表明:该算法具有收敛速度快、解缠效果好等特点.     

M带小波变换在图象中的应用

小波变换是近年来兴起的一种时频域信号分析理论,是信号分析处理的一种强有力的新工具.本文根据小波变换的特点,在Mallat二带多分辨分析的基础上,讨论分析了信号的多带多分辨分析的理论和实现算法,并将这一理论和算法应用于图象处理,取得了满意效果.     

Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction

In this paper, a multiresolution finite-impulse-response (FIR) neural-network-based learning algorithm using the maximal overlap discrete wavelet transform (MODWT) is proposed. The multiresolution learning algorithm employs the analysis framework of wavelet theory, which decomposes a signal into wavelet coefficients and scaling coefficients. The translation-invariant property of the MODWT allows alignment of events in a multiresolution analysis with respect to the original time series and, therefore, preserving the integrity of some transient events. A learning algorithm is also derived for adapting the gain of the activation functions at each level of resolution. The proposed multiresolution FIR neural-network-based learning algorithm is applied to network traffic prediction (real-world aggregate Ethernet traffic data) with comparable results. These results indicate that the generalization ability of the FIR neural network is improved by the proposed multiresolution learning algorithm.