基于小波变换和混合优化算法的图像配准

利用小波变换对图像进行多分辨率分解,用粒子群优化和单纯形结合的混合优化算法对最低分辨率图像进行配准,并仅用单纯形法对较高分辨率图像进行配准。粒子群优化算法全局搜索能力强,单纯形法能有效地进行局部搜索,将两种算法相结合,提高了图像配准的精度。利用小波变换性质逐步缩小搜索范围,提高了配准速度。     

基于多分辨率的快速迭代最近点配准算法

针对迭代最近点(Iterative Closest Point,ICP)算法计算时间长的问题,提出一种基于多分辨率配准点的ICP算法。使用自适应体素网格滤波器对原始点云进行多分辨率采样,利用低分辨率点云快速迭代获得两点云间初始变换矩阵;利用高分辨率点云在初始变换基础上做更精确配准。实验结果表明,该算法在配准精度基本不变的情况下,可以显著降低配准时间,且随着点云点数增加,速度提升效果越明显。     

基于特征点和改进ICP的三维点云数据配准算法

在光学非接触三维测量中,复杂对象的重构需要多组测量数据的配准。最近点迭代(ICP)算法是三维激光扫描数据处理中点云数据配准的一种经典的数学方法,为了获得更好的配准结果,在ICP算法的基础之上,提出了结合基于特征点的等曲率预配准方法和邻近搜索ICP改进算法的精细配准,自动进行点云数据配准的算法,经对牙齿点云模型实验发现,点云数据量越大,算法的配准速度优势越明显,采用ICP算法的运行时间(194.58 s)远大于本算法的运行时间(89.13 s)。应用实例表明:该算法具有速度快、精度高的特点,算法效果良好。     

基于多分辨率和Powell算法的医学图像配准

针对基于互信息和Powell算法存在局部收敛的问题,在改进的Powell算法的基础上,提出了一种基于多分辨率策略的医学图像配准算法。首先通过小波变换对源图像进行分层,然后在最低频带使用改进的Powell算法进行搜索,并利用搜索结果来指导上一层的搜索,逐层细化,由粗到细,最终实现图像的精确配准。实验结果表明,该方法较传统方法速度快、精度高、鲁棒性好,同时能有效避免局部收敛。     

基于区域扩张的三维点云模型配准算法

为提高三维散乱点云自动配准的准确率,提出一种新的基于区域扩张的配准算法。通过局部点云法向量的变化提取特征点,利用区域扩张方法进行初始配准,在搜索精确匹配点的过程中直接剔除错误匹配,使用改进的最近点迭代算法对点云进行精确对齐。实验结果表明,与经典最近迭代点算法和基于曲率的点云自动配准算法相比,该算法能够提升点云配准精度,对特征平滑的点云模型具有较好的效果。     

基于小波的遥感图像全局配准算法研究及其并行实现

随着遥感技术的发展,遥感图像处理领域对自动图像配准技术的需求越来越迫切. 首先提出了点匹配和全局配准相结合的自动图像配准算法;从实现快速配准的途径出发，提 出了利用多分辨率小波缩小搜索空间及进行全局配准的自动算法;详细设计了逐级求精的搜 索策略,并比较总结了算法的特点;在此基础上,提出了两种可行的数据并行方案;最后在一 个小规模的机群系统上实现了上述串、并行算法,给出了客观的性能评价.实验结果表明文 中提出的算法达到了预期的目标,即针对多传感器、大数据量的遥感图像,在保证精度的前 提下,进行快速高效的自动配准.     

一种基于曲率的点云自动配准算法

针对兵马俑破碎俑片虚拟复原过程中拼接效率低的问题,提出了一种新型的基于曲率的散乱点云数据自动配准算法.该算法利用MLS表面计算出两组点云中每个点的曲率,提取局部曲率变化最大的特征点,并计算曲率的Hausdorff距离来获得初始匹配点,然后根据初始匹配点之间极大极小曲率的相似度函数,采用粒子群优化算法确定精确匹配点.最后用四元组法求得坐标变换实现粗配准,并且用迭代最近点算法提高配准精度.实验验证了算法的有效性和稳定性.     

基于小波金字塔和轮廓特征的医学图像配准

提出一种快速而精确的自动医学图像配准算法.先提取图像的轮廓,根据力矩主轴法计算初步配准变换参数的值,实现参考图像和待配准图像的初步配准;然后在此基础上,利用小波分解图像,提取图像的边缘特征点,采用小波金字塔分层搜索的策略在一个相对较小的范围对变换参数进行迭代搜索,最终实现图像的精确配准.不但提高了配准的精度,而且可减少精配准过程中所需的时间,有效地降低了配准过程所需的计算量.由实验结果可以看出,配准结果精确,实现速度快,因此是一种实用的多模态配准算法.     

基于视觉特征的多传感器图像配准

多传感器图像配准在空间图像处理中有非常重要的应用价值,但同时也面临着多源空间数据各异性困难。考虑到图像配准过程中的多分辨率视觉特征,采用基于小波的多分辨率图像分解来指导从粗到细的配准过程,利用扩展的轮廓跟踪算法提取满足视觉特征的轮廓,在轮廓链码曲率函数的基础上实现基于傅里叶变换的多分辨率形状特征匹配。与已有的基于特征的图像配准算法进行实验比较,实验结果表明该方法对于从多传感器得到的异质图像具有良好的配准效果。     

基于Powell算法与改进遗传算法的医学图像配准方法

针对基于互信息图像配准的局部极值问题,提出一种基于Powell算法与改进遗传算法结合的医学图像配准方法。该方法对标准遗传算法存在的收敛速度慢、易早熟、有可能导致误配的缺陷,提出了相应的改进策略; 采用Logistic混沌映射生成迭代过程中的个体; 运用基于小波变换的多分辨率分析策略,采用混合优化算法在图像的最低分辨率层进行全局优化,以全局最优值,结合Powell算法完成医学图像配准。实验结果表明,所提方法可有效避免优化算子陷入局部极值,并提高了配准速度; 相对于纯Powell方法和未改进的遗传算法,配准的精确度和性能更好。     

Some applications of Loop-subdivision wavelet tight frames to?the?processing of 3D graphics

Multiresolution analysis based on subdivision wavelets is an important method of 3D graphics processing. Many applications of this method have been studied and developed, including denoising, compression, progressive transmission, multiresolution editing and so on. Recently Charina and St?ckler firstly gave the explicit construction of wavelet tight frame transform for subdivision surfaces with irregular vertices, which made its practical applications to 3D graphics became a subject worthy of investigation. Based on the works of Charina and St?ckler, we present in detail the wavelet tight frame decomposition and reconstruction formulas for Loop-subdivision scheme. We further implement the algorithm and apply it to the denoising, compression and progressive transmission of 3D graphics. By comparing it with the biorthogonal Loop-subdivision wavelets of Bertram, the numerical results illustrate the good performance of the algorithm. Since multiresolution analysis based on subdivision wavelets or subdivision wavelet tight frames requires the input mesh to be semi-regular, we also propose a simple remeshing algorithm for constructing meshes which not only have subdivision connectivity but also approximate the input mesh.     

Nonlinear and adaptive undecimated hierarchical multiresolution analysis for real valued discrete time signals via empirical mode decomposition approach

Hierarchical multiresolution analysis is an important tool for the analysis of signals. Since this multiresolution representation provides a pyramid like framework for representing signals, it can extract signal information effectively via levels by levels. On the other hand, a signal can be nonlinearly and adaptively represented as a sum of intrinsic mode functions (IMFs) via the empirical mode decomposition (EMD) algorithm. Nevertheless, as the IMFs are obtained only when the EMD algorithm converges, no further iterative sifting process will be performed directly when the EMD algorithm is applied to an IMF. As a result, the same IMF will be resulted and further level decompositions of the IMFs cannot be obtained directly by the EMD algorithm. In other words, the hierarchical multiresolution analysis cannot be performed via the EMD algorithm directly. This paper is to address this issue by performing a nonlinear and adaptive hierarchical multiresolution analysis based on the EMD algorithm via a frequency domain approach. In the beginning, an IMF is expressed in the frequency domain by applying discrete Fourier transform (DFT) to it. Next, zeros are inserted to the DFT sequence and a conjugate symmetric zero padded DFT sequence is obtained. Then, inverse discrete Fourier transform (IDFT) is applied to the zero padded DFT sequence and a new signal expressed in the time domain is obtained. Actually, the next level IMFs can be obtained by applying the EMD algorithm to this signal. However, the lengths of these next level IMFs are increased. To reduce these lengths, first DFT is applied to each next level IMF. Second, the DFT coefficients of each next level IMF at the positions where the zeros are inserted before are removed. Finally, by applying IDFT to the shorten DFT sequence of each next level IMF, the final set of next level IMFs are obtained. It is shown in this paper that the original IMF can be perfectly reconstructed. Moreover, computer numerical simulation results show that our proposed method can reach a component with less number of levels of decomposition compared to that of the conventional linear and nonadaptive wavelets and filter bank approaches. Also, as no filter is involved in our proposed method, there is no spectral leakage in various levels of decomposition introduced by our proposed method. Whereas there could be some significant leakage components in the various levels of decomposition introduced by the wavelets and filter bank approaches.     

基于三角网格模型多分辨分解的图像压缩

构造了图像的三角网格模型.基于三角网格模型的多分辨分解,提出了一个新的图像压 缩方法.通过图像的三角网格模型的多分辨分解、压缩与重构,实现了图像的多分辨解、压缩与重 构.构造了一个在小波图像压缩中未曾使用过的小波滤波器组,该小波滤波器组算法具有O(n)运 行时间.实验表明,该方法能获得较好的图像压缩性能.     

Multiresolution analysis in extraction of reference lines fromdocuments with gray level background

Based on wavelets, a theoretical method has been developed to process multi-gray level documents. In this method, two-dimensional multiresolution analysis, a wavelet decomposition algorithm, and compactly supported orthonormal wavelets are used to transform a document image into sub-images. According to these sub-images, the reference lines of a multi-gray level document can be extracted, and knowledge about the geometric structure of the document can be acquired. Particularly, this approach is more efficient to process form documents with gray level background. Experiments indicate that this new method can be applied to process documents with promising results     

Streaming Surface Reconstruction Using Wavelets

We present a streaming method for reconstructing surfaces from large data sets generated by a laser range scanner using wavelets. Wavelets provide a localized, multiresolution representation of functions and this makes them ideal candidates for streaming surface reconstruction algorithms. We show how wavelets can be used to reconstruct the indicator function of a shape from a cloud of points with associated normals. Our method proceeds in several steps. We first compute a low‐resolution approximation of the indicator function using an octree followed by a second pass that incrementally adds fine resolution details. The indicator function is then smoothed using a modified octree convolution step and contoured to produce the final surface. Due to the local, multiresolution nature of wavelets, our approach results in an algorithm over 10 times faster than previous methods and can process extremely large data sets in the order of several hundred million points in only an hour.     

Adaptive extraction of time-varying isosurfaces

We present an algorithm for adaptively extracting and rendering isosurfaces from compressed time-varying volume data sets. Tetrahedral meshes defined by longest edge bisection are used to create a multiresolution representation of the volume in the spatial domain that is adapted overtime to approximate the time-varying volume. The reextraction of the isosurface at each time step is accelerated with the vertex programming capabilities of modern graphics hardware. A data layout scheme which follows the access pattern indicated by mesh refinement is used to access the volume in a spatially and temporally coherent manner. This data layout scheme allows our algorithm to be used for out-of-core visualization.     

一种强壮的网格水印算法

针对计算机图形学中常用的三角形网格模型,提出了和种新的数字水印方案,这个方案针对网格水印的两个难点：网格的频率分解和受攻击网格的拓扑恢复,与I Guskov的多分辨率网格处理工具臬集成,不需建立额外的数据结构和进行额外的复杂计算,直接在网格低频成中分中嵌入水印;并且在利用网格处理工具箱进行网格处理时,可以较好地保留水印,所使用的网格重采样算法简单高效,使简化网格和拓扑结构已改变的网格的水印检测成为可能,在描述算法的基础上,给出了实验结果及攻击分析,实验表明,该算法具有较好的透明性和强壮性。     

隐式曲面的多分辨率法向网格逼近

法向网格是一种新型的曲面多分辨率描述方式，其中每个层次都可以表示为其前一个粗糙层次的法向偏移．文中提出一种基于法向网格表示的隐式曲面多分辨率网格逼近算法．首先通过基于空间剖分技术的多边形化算法获得隐式曲面的粗糙逼近网格，并利用网格均衡化方法对粗糙网格进行优化，消除其中的狭长三角形；然后利用法向细分规则迭代地对网格中的三角面片进行细分，并利用区间算术技术沿法向方向对隐式曲面进行逼近．最终生成的隐式曲面分片线性逼近网格为法向网格．该逼近网格为隐式曲面提供了一种多分辨率表示，网格具有细分连通性，其数据量较传统的多边形化算法所生成的网格有大幅度的压缩．该算法可用于隐式曲面的多级绘制、累进传输及相关数字几何处理．     

基于小波变换的3维网格水印

3维网格数据是不规则采样的数据集,传统的图像变换域水印算法不能直接应用到网格上.提出了一种小波变换域的鲁棒网格水印算法.该算法对半规则的多分辨率网格进行小波变换,得到一个基网格和一系列小波系数.在对小波系数的统计特性进行实验分析的基础上,选择在低频子带的小波系数中嵌入水印,并对小波系数的切向和法向分量设计了不同的嵌入强度,以减小几何失真.实验结果表明,该水印算法满足不可见性,并具有较强的鲁棒性.