基于模糊信息原理的纹理图像分割方法

提出了一种基于同步自回归（ＳＡＲ） 模型和模糊信息原理进行纹理分割的方法。利用二阶ＳＡＲ 模型对图像纹理进行描述,用最小平方误差方法对模型参数进行估计,在对模型参数分析的基础上,将估计的参数进行改进后作为纹理的特征向量用于纹理图像的分类与分割。由于实际图像带有许多的模糊信息,组成纹理的基元和基元之间的关系也具有很大的模糊性,文中根据模糊信息原理,分析了纹理图像的模糊特性,给出了一种基于模糊贴近度的纹理分割方法。实验结果表明,与常规的距离方法相比,用文中的方法进行图像纹理分割能取得更好的效果。     

CARMA模型参数的两步估计法

本文对噪声未知的ＣＡＲＭＡ模型，用自适应线性增强器与自适应滤波器并联，作ＣＡＲＭＡ模型的噪声估计器，以最小二乘法估计参数，形成了一种适用于ＣＡＲＭＡ模型的两步估计法。     

基于马尔可夫随机场模型和函数联接网络的纹理分类方法

文中提出了利用二阶高斯ＭＲＦ模型对图像纹理进行描述,采用最小平方误差方法进行参数估计,将估计参数作纹理的特征向量,并且利用函数联接网络对特征进行分类。对纹理图像进行的实验表明,采用这种方法能够提高学习速度,简化计算过程,纹理分类能取得较好的效果。     

基于纹理模型的红外图像弱小目标检测

针对红外图像中弱小目标的检测问题，提出了一种基于图像背景纹理分析的检测方法，利用二阶高斯－马尔柯夫随机场（ＧＭＲＦ）模型对背景纹理进行描述，并采用最小平方误差方法进行参数估计，将估计参数作为纹理的特征向量。     

纹理分析窗大小的高斯-马尔可夫随机场模型估计方法

在纹理分析中,窗口大小的选择对所提取特征的有效性及计算速度等有很大影响。文中利用高斯－马尔可夫随机场（ＧＭＲＦ）模型对纹理进行描述,采用最小平方误差估计获取纹理图像的随机场参数,并证明了这种估计的一致性。针对估计式在某些情况下可能无解,对该式作了改进,使其在实际应用中总能有解。利用估计的一致性,提出了一种系统估计纹理分析窗口大小的方法,实验表明了这种方法的有效性。     

一种基于LS自适应AR建模的高分辨率时间延迟估计方法

本文提出了一种基于最小二乘（ＬＳ）自适应ＡＲ建模的时延估计方法。这种方法以接收信号的功率谱密度函数为时间序列，利用最小二乘格型自适应滤波器经由ＡＲ建模而得到高分辨率的时间延迟估计。文中给出了这种方法的原理、具体实现及性能分析，并以相关时延估计方法为参照进行了计算机模拟。     

非高斯有色噪声中的正弦信号频率估计

本文研究非高斯ＡＲＭＡ有色噪声中的正弦信号频率估计问题。利用自相关函数和三阶累积量相结合，提出了一种先估计噪声模型ＡＲ参数，然后对观测值进行预滤波，最后估计信号模型参数的新方法，模拟实验结果表明，新方法具有良好的频率估计性能。     

Shannon小波包分解自适应Gabor滤波器设计及其在纹理分割中的应用

本文首先研究了Ｇａｂｏｒ滤波器进行确定性纹理分割时纹理结构与Ｇａｂｏｒ函数的关系，提出了利用在频域中具有紧支集的Ｓｈａｎｎｏｎ小波包分解检测纹理的主频；最后，提出了基于小波包分解自适应Ｇａｂｏｒ函数与纹理图像卷积，就可以在纹理的连续处产生良好的阶跃边缘，并通过实验验证了该算法的有效性及其对噪声的鲁棒性。     

星载合成孔径雷达（SAR）原始回波信号仿真模型与系统

本文叙述了一种新的星载ＳＡＲ原始回波信号仿真模型，建立了星载ＳＡＲ分布目标模型和空间几何关系模型，分析了姿态误差等对回波信号仿真中多普勒参数的影响，并且通过主位频域的快速积生成距离徒动。     

星载合成孔径雷达多普勒参数估计

本文给出了一种综合的星载合成孔径ＳＡＲ雷达多普勒参数估计方法，该方法可以从雷达回波数据本身提取多普勒进行成像处理，甚至可以不依赖卫星历数据和姿态数据作为初始估计。该方法是基于对多普勒参数及其误差的性质的研究而提出的。最后，利用ＳＥＡＳＡＴ－Ａ海洋卫星的ＳＡＲ原始数据的多普勒参数估计结果和采用该参数进行成像处理的结果，验证了该方法的可行性和有效性。     

Coupled nonparametric shape and moment-based intershape pose priors for multiple basal ganglia structure segmentation

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance images. We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy.     

结合区域分割的SIFT图像匹配方法

针对待配准图在参考图上存在多个相似的区域,传统的SIFT算法导致匹配点数量较少,影响对模型变换参数估计的情况,提出了结合区域分割的SIFT方法。与原始算法相比,该算法可以得到更多正确的匹配点对,时效性上更优。实验结果表明,该算法比原算法的正确匹配点对提高了近30倍,结合区域分割的特征匹配,剔除了90%以上的误匹配点对,改进后的算法时间性能上也更优。     

Improved Soft Iterative Channel Estimation for Turbo Equalization of Time Varying Frequency Selective Channels

In this paper, we present computationally efficient iterative channel estimation algorithms for Turbo equalizer-based communication receiver. Least Mean Square (LMS) and Recursive least Square (RLS) algorithms have been widely used for updating of various filters used in communication systems. However, LMS algorithm, though very simple, suffers from a relatively slow and data dependent convergence behaviour; while RLS algorithm, with its fast convergence rate, finds little application in practical systems due to its computational complexity. Variants of LMS algorithm, Variable Step Size Normalized LMS (VSSNLMS) and Multiple Variable Step Size Normalized LMS algorithms, are employed through simulation for updating of channel estimates for turbo equalization in this paper. Results based on the combination of turbo equalizer with convolutional code as well as with turbo codes alongside with iterative channel estimation algorithms are presented. The simulation results for different normalized fade rates show how the proposed channel estimation based-algorithms outperformed the LMS algorithm and performed closely to the well known Recursive least square (RLS)-based channel estimation algorithm.     

Complex Adaptive LMS Algorithm Employing the Conjugate Gradient Principle for Channel Estimation and Equalization

The Complex Block Least Mean Square (LMS) technique is widely used in adaptive filtering applications because of its simplicity and efficiency from a theoretical and implementation standpoint. However, the limitations of the Complex Block LMS technique are slow convergence and dependence on the proper choice of the stepsize or convergence factor. Moreover, its performance degrades significantly in time-varying environments. In this paper, a novel adaptive LMS technique named the Complex Block Conjugate LMS algorithm, CBC-LMS, is presented. Based on the Conjugate Gradient Principle, the proposed technique searches orthogonal directions to update the filter coefficients instead of the negative gradient directions used in the Complex Block LMS algorithm. In addition, the CBC-LMS algorithm derives optimal stepsizes to adjust the adaptive system coefficients at each iteration. As a result, the developed method overcomes the inherent limitations of the existing Complex Block LMS algorithm. The performance of the CBC-LMS technique is tested in wireless channel estimation and equalization applications, using both computer simulations and laboratory experiments. Furthermore, the developed technique is compared to the Complex Block LMS method and a recently proposed method, which is called Complex Optimal Block Adaptive LMS (OBA-LMS). The experimental and simulation results confirm that the proposed CBC-LMS technique achieves faster convergence with comparable accuracy and reduced computational complexity, relative to the existing techniques.     

一种基于LMS自适应滤波的互相关时延估计优化算法

在多站时差定位系统中使用基于LMS自适应滤波的互相关法进行时延估计时,若采用固定步长因子则会在收敛速度和稳态失调之间存在较大矛盾,从而影响时延估计精度。针对这一问题,文中提出了一种基于分段变步长LMS自适应滤波和希尔伯特差值的互相关时延估计优化算法。该方法首先采用分段变步长LMS自适应滤波对信号进行滤波处理,然后将滤波后的信号作互相关运算,最后通过希尔伯特差值法锐化相关函数的峰值,进一步提高时延估计精度。在相同条件下,文中模拟分析了不同算法的时延估计精度。实验结果表明,新的优化算法时延估计精度更高。在不同信噪比下,新方法相较传统时延估计方法精度提高了2.2%以上,具有良好的抗噪声性能。     

Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI

Automatic extraction of vertebra regions from a spinal magnetic resonance (MR) image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation system, which consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. In order to produce an efficient and effective vertebra detector, a statistical learning approach based on an improved AdaBoost algorithm is proposed. A robust estimation procedure is applied on the detected vertebra locations to fit a spine curve, thus refining the above vertebra detection results. This refinement process involves removing the false detections and recovering the miss-detected vertebrae. Finally, an iterative normalized-cut segmentation algorithm is proposed to segment the precise vertebra regions from the detected vertebra locations. In our implementation, the proposed AdaBoost-based detector is trained from 22 spinal MR volume images. The experimental results show that the proposed vertebra detection and segmentation system can achieve nearly 98% vertebra detection rate and 96% segmentation accuracy on a variety of testing spinal MR images. Our experiments also show the vertebra detection and segmentation accuracies by using the proposed algorithm are superior to those of the previous representative methods. The proposed vertebra detection and segmentation system is proved to be robust and accurate so that it can be used for advanced research and application on spinal MR images.     

Analysis of LMS-adaptive MLSE equalization on multipath fadingchannels

We consider a practical maximum-likelihood sequence estimation (MLSE) equalizer on multipath fading channels in conjunction with an adaptive channel estimator consisting of a least mean square (LMS) estimator and a linear channel predictor, instead of assuming perfect channel estimates. A new LMS estimator model is proposed which can accurately characterize the statistical behavior of the LMS estimator over multipath fading channels. Based on this model, a new upper-bound on block error rate is derived under the consideration of imperfect channel estimates. Computer simulations verify that our analytical results can correctly predict the real system performance and are applicable over a wide range of the step size parameter of the LMS estimator     

一种新的无线通信随机相位线性均方估值方案

本文提出了一种新的无线通信随机相位线性均方估值方案，分析了其性能；针对瑞利度落信道进行了计算机模拟，并将模拟结果与理论值进行了比较。理论分析和模拟结果表明，该方法是一种有效的无线通信随机相位估值方法。     

一种基于视觉感知及相位特征的纹理分割新方法

本文提出了一种基于视觉感知机理和相位特征的新型纺理分割方法，它由方向滤波器设计、局部非缠卷相位信息提取和可变金字塔分割三部分组成，通过有代表性的仿真实验验证了其正确性和有效性。     

太赫兹光场成像的深度估计方法与实验研究

在前期工作中,通过对太赫兹光场图像进行离散余弦变换(Discrete Cosine Transform,DCT)滤波和数字重聚焦,初步实现了图像去噪和前后景分割。为了进一步得到质量更高的太赫兹光场原数据并达到更加精确的深度分割效果,改进了实验方案及处理方法,并提出了一种基于极平面图像(Epipolar Plane Image,EPI)的太赫兹光场深度估计方法。在太赫兹图像特性的基础上,给出了深度与视差的关系,并利用局部视差和置信度构建了全局深度图,从而达到了深度估计的目的。最后,在实验中通过10×10的相机阵列采集太赫兹光场数据,得到了准确聚焦于不同平面的重聚焦结果和高分辨度的深度估计图,实现了太赫兹光场成像的深度估计。