基于改进的多分辨率Hough变换的直线检测方法

该文提出了一种基于改进的多分辨率Hough变换的直线检测方法。对多分辨率图像由粗到细地循环处理，首先利用全局累加器序列对最小的图像做Hough变换得直线参数，利用该直线参数估计下一循环图像中的直线参数；建立了一种简单对应关系，将得到的直线参数的估计范围转换成图像空间范围；然后在图像空间范围内利用局部累加器对较大图像做Hough变换检测直线。最后还给出了线段端点检测、长度计算及连续性考察的方法。实验表明，该方法可以加快直线检测速度、节省存储空间，端点检测方法具有一定的鲁棒性。     

基于统计方差的Hough变换峰值检测方法

针对Hough变换累积矩阵存在的峰值簇拥现象,提出了一种新的Hough变换峰值检测方法.通过对累加器参数空间的分析,考虑到噪声、误差引起的单元累积值的不确定性,在局部最大值周围区域,把每列投票单元看作是随机变量,选用每列的统计方差进行拟合、插值,从而检测到精确的峰值参数.实验结果验证了方法的有效性,与传统方法相比,具有更好的鲁棒性和精确性.     

基于麦克风阵列的语音增强算法

为解决现有语音增强算法需要麦克风数量较多和受估计误差影响较大的问题,提出一种改进的声源定位和波束形成方法。在现有声源定位算法利用时间延迟的基础上,引入能量衰减参数,实现利用双麦克风进行声源定位的目标;在波束形成算法中引入加载系数,在出现协方差矩阵统计失配时仍可对期望方向聚焦,提高波束形成算法的鲁棒性。仿真结果表明,改进后的算法与传统算法相比具有更强的鲁棒性。     

一种低复杂度的鲁棒自适应波束形成算法

传统基于干扰噪声协方差矩阵（interference-plus-noise covariance matrix,INCM）重构的鲁棒自适应波束形成（robust adaptive beamformer,RAB）算法在多种样本数据协方差矩阵误差和信号导向向量误差的失配环境中具有较强的鲁棒性,但目前主流的INCM重构法都是对信号和干扰的导向向量通过建立凸优化模型来估计,这带来了很高的计算复杂度。为了解决这个问题,提出了一种低复杂度的基于INCM重构的RAB算法。该算法首先将干扰信号的导向向量分解为对应标称项和误差项的和,然后通过一种子空间方法估计得到误差项的单位向量。接下来对一个Capon空间谱功率最大问题进行求解,得到误差项的模值,以此得到重构的INCM。同时利用Capon空间谱中残差噪声的存在,使用交替投影法估计得到期望信号的导向向量,最后得到所提算法的权重向量。仿真实验表明所提算法在多种误差环境下具有较强鲁棒性的同时,还具有较低的计算复杂度。     

基于建立序列图像间映射关系的匹配算法

提出一种基于图像间映射关系的匹配算法，该算法利用目标的运动函数映射前后两幅图像的特征点，映射误差函数最小的对应即为正确的匹配。引进特征点接关系的约束，从而大大减少了候选匹配点的构造空间。实验结果表明，该算法具有良好的鲁棒性和实时性     

基于输出反馈的鲁棒控制器优化设计及仿真

传统的控制系统,利用极点配置后所剩余的自由度来增强系统的鲁棒性,但解析算法复杂,在实际工程中难以应用.为优化算法,提高极点配置精度,提出了一种能满足工程实际要求的利用输出反馈来配置系统特征值和实现系统鲁棒性能最优的逼近算法.方法通过选定一种反应极点配置精度和表征系统鲁棒性的目标函数,利用最优化中的Powell算法,在实现期望特征值的近似任意配置的同时,提高了系统的鲁棒性能.方法具有易于实现和适用面广等特性.并以水翼艇纵向运动控制系统为例进行了仿真,取得了令人满意的结果.     

一种自适应的月球车驱动控制算法

月球车的驱动控制是控制月球车按照期望速度运动的控制器。为使月球车快速稳定地达到期望速度,要求控制算法具有很强的鲁棒性,并且使系统的响应速度尽可能的快。论文提出一种方法:通过单神经元自适应算法求出控制月球车的合力和合力矩,再利用拉格朗日算法求得每个执行器的控制量。仿真实验表明此算法有很好的鲁棒性和自适应性,为月球车的控制应用提供了理论依据。     

基于梯度相位编组的树枝识别新算法

为了能够快速准确地计算出果树枝干的空间位置,提出了一种基于梯度相位编组的Hough变换树枝检测新算法。利用改进的平方梯度法计算边缘点的梯度相位角,找出梯度相位直方图中多个阈值大于T的峰值。然后将具有相近梯度相位角的边缘点归为一组。最后对每组中的边缘点采用改进的两点表决Hough变换算法找出对应的直线参数。利用梯度相位角进一步验证参数的正确性。研究结果表明,提出的梯度相位编组直线检测方法具有速度快、检测误差小和鲁棒性强的特点,能够快速准确地实现果树树枝的定位和检测,对部分被遮挡的树枝的识别效果也较好。     

核空间隐马尔可夫随机场图像模糊聚类

针对模糊C均值算法未考虑图像邻域信息,导致其分割效果不好的不足,结合隐马尔可夫随机场和高斯核函数,提出核空间隐马尔可夫随机场模糊C均值聚类算法。引入隐马尔可夫随机场,在目标函数中引入像素的空间邻域信息,使得分割算法对噪声鲁棒性增强;引入核函数,将样本点非线性变换映射到高维特征空间,增强图像分割的抗干扰能力,保持图像的细节信息。对标准灰度图像添加噪声,用以验证算法的性能。视觉效果及分割图像的峰值信噪比均显示,改进算法具有更好的抗噪能力。     

一种高精度鲁棒的基于直线对应的位姿估计迭代算法

2D-3D特征对应位姿估计问题算法多基于点对应,而基于直线对应求解比基于点对应求解更具有优势。从欧氏空间这一新颖角度出发,提出了一种从直线对应求解位姿估计问题的迭代算法,算法思想是先迭代求解出最优的旋转矩阵,然后再得到平移向量。针对不同的直线组情形,给出了相应的迭代初始值计算方法。仿真实验数据表明,算法具有对初始值较不敏感、高精度、鲁棒性好等特点。     

基于ACF的小波域数字图像水印算法

设计了一种基于自相关函数（ACF）的盲检测小波域数字图像水印算法,提取原图像小波分解的高频细节分量信息,并增强其相关性后,对水印信息进行调制掩盖。经过处理的掩藏了水印的高频细节分量与原图像的逼近分量再合成,得到含水印的图像。水印检测时,将含水印图像小波分解,分析其细节分量的ACF峰值,对图像的几何攻击进行估计及校正,以取得水印同步,再通过水印相关性进行检测评价,实验表明,该方法可行,具有较强的抗剪切、几何攻击性。     

一种改进的孤立词端点检测方法

将Mel倒谱距离和多带能量-熵特征相结合,提出了一种改进的孤立词端点检测方法。该方法具有不需要估计背景噪声调整门限阈值的优点。仿真实验表明,所提方法在实验室噪声环境下可检测到准确的孤立词端点,特别是当起止处含有弱的摩擦音或爆破音时,不会造成虚检和漏检,鲁棒性较好。由于计算简单,适合实时应用。     

机载光电稳瞄平台的扰动观测分数阶控制方法

随着机载光电稳瞄平台在国防军事领域的广泛应用,现代武器系统对机载光电稳瞄平台控制技术的要求越来越高.鉴于此,结合分数阶控制和扰动观测器原理,提出一种基于扰动观测的分数阶控制方法,用于提高机载光电稳瞄平台控制系统的各项性能.给出相应的闭环控制系统结构和控制器设计方法,并对其输入输出特性、扰动抑制能力等性能进行研究.在阶跃响应、正弦跟踪、扰动抑制和鲁棒性等仿真实验中,所提出控制器均表现良好.仿真结果表明,所提出基于扰动观测的分数阶控制器不仅具有分数阶控制响应速度快、鲁棒性强的特性,还表现出扰动观测器抗扰能力强的优点,可以达到提高机载光电稳瞄平台控制系统性能的目的.     

A novel Hough transform method for line detection by enhancing accumulator array

In this paper, an improved Hough transform (HT) method is proposed to robustly detect line segments in images with complicated backgrounds. The work focuses on detecting line segments of distinct lengths, totally independent of prior knowledge of the original image. Based on the characteristics of accumulation distribution obtained by conventional HT, a local operator is implemented to enhance the difference between the accumulation peaks caused by line segments and noise. Through analysis of the effect of the operator, a global threshold is obtained in the histogram of the enhanced accumulator to detect peaks. Experimental results are provided to demonstrate the efficiency and robustness of the proposed method.     

Robustification as a tool in modeling biochemical reaction networks

Biological functions have evolved to become robust against a multitude of perturbations such as gene mutations, intracellular noise and changes in the physical and chemical environment. This robustness should be reflected in models of the underlying biochemical networks, and robustness analysis has consequently been established as an important tool for model validation in systems biology. However, while robustness analysis can be used to invalidate a given model, it does not support the postulation of model modifications that can serve to improve the robustness. Herein we propose a method for this purpose, based on computing the sensitivity of the robustness with respect to generic dynamic perturbations applied to the individual network edges. To quantify robustness we compute the smallest simultaneous change in the activity of the network nodes that induces a bifurcation in the network, resulting in a qualitative change in the network behavior. The proposed method can be used to identify network interactions with the most significant impact on the overall robustness of a given function. By considering the impact of adding new nodes and edges, the method can also be used to postulate the presence of important unmodeled components and interactions. The focus here is on biological functions related to bistable switches and sustained oscillations, and the proposed methodology is demonstrated through application to metabolic oscillations in white blood cells and bistable switching in MAPK signal transduction. The classical Goodwin model of oscillations in a simple gene regulatory network is used for illustration throughout the paper.     

Using selective correlation coefficient for robust image registration

A new method is proposed for robust image registration named Selective Correlation Coefficient in order to search images under ill-conditioned illumination or partial occlusion. A correlation mask-image is generated for selecting pixels of an image before matching. The mask-image can be derived from a binary-coded increment sign-image defined from any object-image and the template. The mask-rate of occluded regions is theoretically expected to be 0.5, while unoccluded regions have much lower rate than 0.5. Robustness for ill-conditioned environment can be realized since inconsistent brightness of occluded regions can be omitted from the mask operation. Furthermore, the mask enhancement procedure is proposed to get more stable robustness. The effectiveness of masking increases by the procedure, resulting in the rate around 0.7 for masking of occluded regions. This paper includes a theoretical modeling and analysis of the proposed method and some experimental results with real images.     

面向图卷积神经网络鲁棒防御方法

近来对图卷积神经网络（GCNs）的研究及其应用日益成熟,虽然它的性能已经达到很高的水准,但GCNs在受到对抗攻击时模型鲁棒性较差。现有的防御方法大都基于启发式经验算法,没有考虑GCNs结构脆弱的原因。最近,已有研究表明GCNs脆弱的原因是非鲁棒的聚合函数。本文从崩溃点和影响函数抗差性角度出发,分析平尾均值函数和均值聚合函数二者的鲁棒性。平尾均值相较于均值函数,其崩溃点更高。平尾均值的影响函数跳跃有界,可抵抗异常值;而均值函数的影响函数无界,对异常值十分敏感。随后在GCNs框架的基础上,通过将图卷积算子中的聚合函数更换为更为鲁棒的平尾均值,提出一种改进的鲁棒防御方法WinsorisedGCN。最后采用Nettack对抗攻击方法研究分析所提出的模型在不同扰动代价下的鲁棒性,通过准确率和分类裕度评价指标对模型性能进行评估。实验结果表明,所提出的防御方案相较于其他基准模型,能够在保证模型准确率的前提下,有效提高模型在对抗攻击下的鲁棒性。     

Part-based methods for handwritten digit recognition

In this paper, we intensively study the behavior of three part-based methods for handwritten digit recognition. The principle of the proposed methods is to represent a handwritten digit image as a set of parts and recognize the image by aggregating the recognition results of individual parts. Since part-based methods do not rely on the global structure of a character, they are expected to be more robust against various deformations which may damage the global structure. The proposed three methods are based on the same principle but different in their details, for example, the way of aggregating the individual results. Thus, those methods have different performances. Experimental results show that even the simplest part-based method can achieve recognition rate as high as 98.42% while the improved one achieved 99.15%, which is comparable or even higher than some state-of-the-art method. This result is important because it reveals that characters can be recognized without their global structure. The results also show that the part-based method has robustness against deformations which usually appear in handwriting.     

基于无迹卡尔曼滤波估算电池SOC

为了实现在线估计汽车动力电池的荷电状态（SOC）,提出了结合神经网络的无迹卡尔曼滤波算法。以Thevenin电路为等效电路模型,建立了状态空间表达式,采用最小二乘算法对模型参数进行辨识。在此基础上,利用神经网络算法拟合电池的荷电状态与模型各个参数之间的函数关系,经过多次实验,确定了神经网络算法的收敛曲线,此方法比传统的曲线拟合精度高。介绍了扩展卡尔曼滤波和无迹卡尔曼滤波的原理,并设计了等效电路模型验证实验、电池的SOC测试实验和算法的收敛性实验。实验结果表明,在不同的工况环境下,该方法估计SOC具有可在线估算、估算精度高和环境适应度高等优点,最大误差小于4%。最后验证了结合神经网络的无迹卡尔曼滤波的算法具有较好的收敛性和鲁棒性,可以有效解决初值估算不准确和累计误差的问题。     

Rich-information watermarking scheme for 3D models of oblique photography

The security issues of copyright information arise rapidly along with the widely application of 3D models of oblique photography. In this paper, a reversible watermarking scheme is proposed for 3D models of oblique photography, aiming at robustness enhancing and rich-information watermark embedding. To realize the goal of rich-information watermark embedding, an encoding method of original copyright information is designed based on QR code, which can shorten significantly the length of the final watermark data without affecting the copyright expression. A 3D point grouping method is designed and both watermark embedding and extraction are completed group by group, so that the robustness of this scheme under certain attacks can be enhanced, e.g., model segmentation, data compression, point randomly deleting, etc. Moreover, watermarks are embedded into spherical coordinates of 3D points so that the ability of the proposed scheme in resisting geometric transformation can be improved. Results of simulation experiments have demonstrated that the proposed scheme can support rich-information watermark embedding, and it has a satisfying robustness under common geometric and non-geometric attacks.