基于图像引力和Mumford-Shah模型的曲线演化算法

提出了一种图像引力和Mumford—Shah（M—S）模型的快速曲线演化算法,该算法分两步实现：首先利用图像引力,对参数主动轮廓线模型中的控制点进行寻优,使控制点能快速地收敛到图像的边缘附近;然后利用插值算法,得到目标较粗糙的大致轮廓,再使用M—S模型和窄带方法得到准确的轮廓。该算法避免了参数主动轮廓线模型不能收敛到物体凹陷的边缘和对初始位置敏感等问题,也避免了M—S模型需要对所有图像数据进行计算,计算量大等问题,实现了对参数和几何主动轮廓线模型进行了有效结合。     

图像放大中的两种FIR内插滤波器实现结构及其抗混叠效果比较

简述了FIR内插滤波的两种高效结构,说明两种内插滤波器的优缺点.针对图像放大中出现的混叠现象,采用相同的参数和参考图像,在视频缩放模块中运用两种不同类型的内插滤波器进行效果对比,得到预期的效果.     

Image sequence interpolation based on optical flow, segmentation, and optimal control

When using motion fields to interpolate between two consecutive images in an image sequence, a major problem is to handle occlusions and disclusions properly. However, in most cases, one of both images contains the information that is either discluded or occluded; if the first image contains the information (i.e., the region will be occluded), forward interpolation shall be employed, while for information that is contained in the second image (i.e., the region will be discluded), one should use backward interpolation. Hence, we propose to improve an existing approach for image sequence interpolation by incorporating an automatic segmentation in the process, which decides in which region of the image forward or backward interpolation shall be used. Our approach is a combination of the optimal transport approach to image sequence interpolation and the segmentation by the Chan-Vese approach. We propose to solve the resulting optimality condition by a segregation loop, combined with a level set approach. We provide examples that illustrate the performance both in the interpolation error and in the human perception.     

Three-dimensional segmentation and interpolation of magnetic resonance brain images

The authors propose a method for the 3-D reconstruction of the brain from anisotropic magnetic resonance imaging (MRI) brain data. The method essentially consists in two original algorithms both for segmentation and for interpolation of the MRI data. The segmentation process is performed in three steps. A gray level thresholding of the white and gray matter tissue is performed on the brain MR raw data. A global white matter segmentation is automatically performed with a global 3-D connectivity algorithm which takes into account the anisotropy of the MRI voxel. The gray matter is segmented with a local 3-D connectivity algorithm. Mathematical morphology tools are used to interpolate slices. The whole process gives an isotropic binary representation of both gray and white matter which are available for 3-D surface rendering. The power and practicality of this method have been tested on four brain datasets. The segmentation algorithm favorably compares to a manual one. The interpolation algorithm was compared to the shaped-based method both quantitatively and qualitatively.     

分数倍内插成形滤波器设计及实现

提出了一种分数倍内插成形滤波器的实现方法并在FPGA中实现。该分数倍内插成形滤波器实现了输入速率与内插成形滤波后速率之间分数倍转换,能够适应输入速率实时调整,突破了传统整数倍内插成形滤波器对输入速率的限制。实现了输入速率为1 KS/s~50 MS/s,步进为1 S/s,输出为100~2 000 MS/s,内插倍数4,分数时延精度为Tclk65 536的滤波器。该分数倍内插成形滤波器硬件资源开销小、接口简洁、灵活性和适用性强,还可根据需要扩展变速率范围。     

Enhanced time-frequency analysis of VAG signals by segmentation and denoising algorithm

An enhanced time-frequency analysis of vibroarthrographic (VAG) signals is devised using segmentation by the dynamic time warping and denoising algorithm by the singular value decomposition, and the normal and abnormal VAG signals are classified by a back-propagation neural network. A total of 1408 VAG segments (normal 1031, abnormal 377) were used for evaluating the performance of the devised method and, consequently, the average accuracy was 92.0 + 1.6% (ranging from 89.4 to 95.4). This method could be used as a complementary tool for the non-invasive diagnosis of joint disorders.     

基于KNN的DSA图像去噪及GPU的快速实现

为快速地去除或减少DSA(Digital Subtraction Angiography)图像的噪声,对比评价KNN(K Nearest Neighbors)算法对高斯噪声、泊松噪声、斑点噪声、椒盐噪声4种噪声去除或减少的效果,帮助医生快速准确地为病人诊断疾病.提出的算法主要贡献在于构建了基于GPU(Graphics Processing Unit)的加速方法,使传统图像去噪的运算速度得到大幅提升.基于图像降质、图像还原过程建模,使用KNN算法对4种噪声去除或减少,并对算法做并行化处理,利用GPU加速实现去噪的过程.通过实验得出,KNN算法能较好地去除或减少高斯噪声、泊松噪声来还原DSA图像,使用CUDA(Compute Unified Device Architecture)编写可在GPU上运行的程序,利用GPU对1 024×1 024像素的24位深度的DSA图像去噪,平均渲染帧率能达到190.53 f/s(帧/秒),较传统CPU(Central Processing Unit)串行,平均处理速度提高70.86倍.使用GPU加速能够快速地处理数据量较大、计算密集的DSA噪声图像,实现有效并且快速的高斯噪声去除,帮助医生精、准、快地诊断疾病.     

基于Lucene的中文分词算法研究与实现

本文重点研究了如何改进中文分词算法,并根据新的中文算法,设计出可以满足Hadoop文件系统可视化文件搜索引擎研究的中文分析器MyAnalyzer。     

网络危险数据细分控制器的设计与实现

受到步进电机低频共振特性的影响,导致以往设计的网络危险数据控制器的控制效率和控制精确度都较低。因此,设计网络危险数据细分控制器。网络数据传入控制器中的步进电机后会产生电流波动,A4988细分芯片对该电流进行细分控制。电源模块为A4988细分芯片的控制工作提供电压转换,维持细分芯片在标准电压下的正常工作。数据筛选模块包括收发电路和分类电路,收发电路对经A4988细分芯片控制后的网络数据进行接收,分类电路将其中的危险数据筛选出来,再经由收发电路传输给使用者。软件构建步进电机对网络数据的细分控制模型,实现控制器的高效、精准控制。实验结果表明,所设计的控制器具有较高的控制效率和控制精确度。     

NGN architecture: generic principles, functional architecture, and implementation

This article summarizes the architectural aspects of a next-generation network in terms of general principles, functional representation, and typical implementation. According to the general reference model, which assumes decoupling of services and transport, NGN can be represented by multiple functional groups. One of the key implementations for session-based services, utilizing an IP multimedia subsystem, is introduced with enhanced features to meet both fixed and mobile network requirements.     

基于OpenCV的图像阈值分割研究与实现

图像的阈值化在图像分割中有着至关重要的作用。在OpenCV算法环境下,运用固定阈值化和自适应阈值化算法,分别对同一幅图调用OpenCV中相应的函数进行处理;同时针对图像中的噪声,结合高斯模糊算法对图像噪声的滤除作用,将高斯模糊和阈值化算法结合起来,分析研究图像分割算法。结果表明自适应阈值化算法可以更有效地进行图像分割。     

基于MMSEG算法的中文分词技术的研究与设计

文中提出了一种更为有效和准确的中文分词处理方法,并利用Lucene软件工具设计了相应的中文分词技术模块。该模块加入了改进的FFM算法,即MMSEG算法[1]的中文分词技术,对中文分词中的歧义现象实现了更好的解决方案。与现有实现算法相比,该方法可以更快更好地实现中文分词功能,可有效提高全文检索中的中文检索的能力。     

量子衍生差分进化算法的设计与实现

为提高差分进化算法的优化性能,从研究差分进化算法的实现机制入手,提出将差分策略与量子比特在Bloch球面的绕轴旋转相融合的新思想。个体采用基于Bloch球面描述的量子比特编码,采用差分策略计算当前个体上量子比特的旋转角度,采用向量积理论构造旋转轴,采用泡利矩阵构造旋转矩阵,以当前最优个体上相应量子比特为目标,在Bloch球面上沿旋转轴向目标比特旋转。采用Hadamard门实现个体变异。函数极小值优化的仿真结果表明,所提方法单步迭代的平均时间约为普通差分进化算法的13倍。当限定步数相同时,优化结果约为普通差分进化算法的0.3倍,当运行时间相同时,优化结果约为普通差分进化算法的0.4倍。从而表明所提算法计算效率降低,但寻优能力明显提高,整体优化性能优于原算法。      

Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing

We present a general wavelet-based denoising scheme for functional magnetic resonance imaging (fMRI) data and compare it to Gaussian smoothing, the traditional denoising method used in fMRI analysis. One-dimensional WaveLab thresholding routines were adapted to two-dimensional (2-D) images, and applied to 2-D wavelet coefficients. To test the effect of these methods on the signal-to-noise ratio (SNR), we compared the SNR of 2-D fMRI images before and after denoising, using both Gaussian smoothing and wavelet-based methods. We simulated a fMRI series with a time signal in an active spot, and tested the methods on noisy copies of it. The denoising methods were evaluated in two ways: by the average temporal SNR inside the original activated spot, and by the shape of the spot detected by thresholding the temporal SNR maps. Denoising methods that introduce much smoothness are better suited for low SNRs, but for images of reasonable quality they are not preferable, because they introduce heavy deformations. Wavelet-based denoising methods that introduce less smoothing preserve the sharpness of the images and retain the original shapes of active regions. We also performed statistical parametric mapping on the denoised simulated time series, as well as on a real fMRI data set. False discovery rate control was used to correct for multiple comparisons. The results show that the methods that produce smooth images introduce more false positives. The less smoothing wavelet-based methods, although generating more false negatives, produce a smaller total number of errors than Gaussian smoothing or wavelet-based methods with a large smoothing effect.     

基于SRG的快速图像分割算法研究与实现

针对传统的SRG算法实时性和准确性不足,提出了一种模拟自然界生态中病毒感染过程的智能快速图像分割算法,并在VC＋＋6平台上实现。该算法基于传统SRG图像分割原理,一方面可以自动探测生长点,并且可根据背景的亮暗动态更新参数,对光线不均匀的图像也可进行分割;另一方面仅通过一次对图像的全局遍历搜索,便可逐一将目标从背景中分割并且作标记。实验结果表明,与传统阈值算法相比,该算法在提高图像分割速度的同时,在不同背景中分割图像的准确性更高。     

Tensor splines for interpolation and approximation of DT-MRI with applications to segmentation of isolated rat hippocampi

In this paper, we present novel algorithms for statistically robust interpolation and approximation of diffusion tensors-which are symmetric positive definite (SPD) matrices-and use them in developing a significant extension to an existing probabilistic algorithm for scalar field segmentation, in order to segment diffusion tensor magnetic resonance imaging (DT-MRI) datasets. Using the Riemannian metric on the space of SPD matrices, we present a novel and robust higher order (cubic) continuous tensor product of B-splines algorithm to approximate the SPD diffusion tensor fields. The resulting approximations are appropriately dubbed tensor splines. Next, we segment the diffusion tensor field by jointly estimating the label (assigned to each voxel) field, which is modeled by a Gauss Markov measure field (GMMF) and the parameters of each smooth tensor spline model representing the labeled regions. Results of interpolation, approximation, and segmentation are presented for synthetic data and real diffusion tensor fields from an isolated rat hippocampus, along with validation. We also present comparisons of our algorithms with existing methods and show significantly improved results in the presence of noise as well as outliers.     

基于自适应像素分割算法的FPGA设计与实现

为了解决运动目标前景检测的精度问题,提出了一种基于FPGA实现的自适应像素分割系统。该系统通过构建新的背景模型和前景分割检测技术优化检测结果,并对传统的自适应像素分割算法进行了调整和修改,以便在FPGA平台上进行硬件实现。在Xilinx virtex7 FPGA芯片上已完成了硬件测试。试验测试结果显示相比其他算法,本文提出设计的各项性能指标均表现良好,检测精度达到71.4%,平均功耗为6.452 W。能够实现以50 frame/s,实时处理分辨率为720×576的视频流。     

基于DES的无刷交流电机控制系统FPGA设计与实现

本文介绍了一种基于离散事件系统（DES）的无刷交流（BLAC）电机的电流控制系统,提出系统的控制器带有图形用户界面(GUI),能够直接显示出有限状态机（FSM）的状态。通过应用FPGA对原有DES控制系统进行改进,将这五个选定的矢量视为的DES离散状态,合理设置FSM选择矢量规则,实现了限定所有可能的方向。在Xilinx Spartan 3E FPGA平台进行的硬件测试结果显示,本文提出的方法减少了60%以上的开关变化数量和开关损耗,证明了提出的DES改进方法对负载变化具有健壮性。     

一种基于区域的交互式MRF图像分割算法设计与实现

提出一种基于区域的交互式MRF图像分割算法,有效地解决了传统MRF方法中初始参数不易准确估计的问题,并能自动检测和矫正出现的误分割。算法在meanshift预分割得到的过分割区域上进行人工标记,并将图像转化到L*u*v空间,用较简单的欧式距离表达不同像素点之间的差异。同时,分析了误分割可能出现的情况,分类别对误分割现象进行自动检测,然后运用基于区域势能的区域合并方法对误分割进行自动矫正。通过对医学图像及自然彩色图像进行实验,实验结果表明该算法能准确地完成分割任务,并实现对误分割现象的自动检测和矫正,与传统MRF及Graph Cut算法相比,该算法能获得更准确及更平滑的分割结果。