摄像机运动情况下的运动对象检测

在监控应用中,由于场景是已知的,因此可以使用背景减去法检测运动对象.当摄像机进行扫描和倾斜运动时,需要使用多个图像帧才能完整地表示监控场景.如何组织和索引这些背景帧属于摄像机跟踪问题.提出一种无需摄像机标定的背景帧索引和访问方法.这一方法需要使用图像配准技术估计图像初始运动参数.提出一种屏蔽外点的图像配准算法,综合利用线性回归和稳健回归快速估计初始运动参数.为了快速计算连续帧之间的运动参数,提出一种基于四参数模型的优化算法.利用非参数背景维护模型抑制虚假运动象素.室内和户外实验结果表明本文方法是有效的.     

基于特征预测与邻域一致性的视频特征快速配准

基于视频图像特征点配准的目标跟踪算法无法兼顾精确性、实时性和鲁棒性,针对该问题,提出一种基于特征位置预测与邻域一致性约束的视频特征快速配准算法。以标注点与目标标记框为模板,通过ORB特征匹配与邻域一致性检验,获得帧间标注点集的对应关系,并计算点集间的尺度变换以确定当前目标框,利用多帧已知标注点位置信息与运动连续性进行多项式回归预测,得到标注点集的位置。在此基础上,对特征点进行局部搜索、提取和描述,根据邻域一致性约束,利用邻域内的支持特征点集实现标注点的稳健匹配。实验结果表明,该算法可对多姿态目标特征点进行配准,与GMS、ORB、SIFT和SURF算法相比,该算法的实时性、准确性和鲁棒性明显提高。     

基于三参数模型的视频图像配准方法研究

视频图像配准是运动视频处理中的一项关键技术。提出了一种新的基于3参数模型的配准算法,该算法利用多尺度的角点检测方法从相邻两帧中抽取特征点,选用Hausdorff距离对特征点集进行匹配,计算3参数模型参数,从而实现图像配准。与传统的6参数仿射模型相比,模型参数的降低并没有显著降低配准效果,同时由于参数搜索空间的减少,该方法用更快的速度得到较准确的结果。     

基于背景配准的矿井危险区域视频目标检测算法

针对矿井危险区域视频监控视场背景复杂,难以实现视频目标精确提取的问题,提出了一种基于背景配准的视频目标检测算法。该算法实现步骤:提取SIFT特征点,计算特征点区域H-S光流矢量;通过区域运动特性分析提取出背景运动区域,对背景运动区域特征点做帧间匹配;计算仿射参数,配准差分后提取出精确的目标区域。实验结果表明,该算法能够去除前景目标特征点对背景配准的影响,可获得较为精确的目标区域。     

一种整体的视频匹配方法

给出一种视频时空配准的整体方法,提出一种视频内匹配与视频间匹配相结合的空间配准策略,改进动态时间扭曲方法以用于时间维的对齐.视频内匹配跟踪视频内各帧图像的特征点并记录其轨迹,视频间匹配配准不同视频的帧图像,使用轨迹对应提供图像配准所需的初始特征点对应,根据图像配准得到的特征点对应建立和更新轨迹对应.该匹配策略充分利用了视频的连贯性提高了匹配的稳定性和效率,同时提高了配准视频的连贯性.改进的动态时间扭曲方法通过极小化两段视频的整体距离建立视频之间的帧对应关系,保持视频内部各帧之间的时序关系并能处理非线性偏移     

一种基于压缩域的视频拼接算法

提出了一种基于压缩域的双摄像机视频拼接算法。首先利用相位相关法估算输入视频的对应第一帧重叠区域，并在重叠区域进行SIFT角点检测和匹配，加快角点匹配速度，提高匹配稳健性，使用RANSAC算法去除外点，采用奇异值分解法配合LM非线性优化方法求解变换参数，得到首帧的对应投影矩阵；对于非首帧的配准，利用压缩视频中的当前帧与前帧的运动矢量，获得全局运动矢量，然后结合对应前帧的投影矩阵，获得相应的当前帧的投影矩阵;最后使用多频带融合算法进行图像混合以改善线性加权融合算法带来的高频细节模糊。与传统算法相比，由于省去了特征提取和匹配方法，从而减少了大量的计算步骤和时间，提高了速度，增加了实用性。实验结果表明该算法具有较好的实用价值。     

基于相邻帧补偿的高速运动目标图像稳像算法及仿真

在高速运动目标图像视频采集过程中,高速运动、风力作用等因素将导致视频图像抖动。为提高高速运动目标图像视觉系统采集性能,改善图像采集质量,提出一种基于相邻帧补偿的高速运动目标图像稳像算法。结合自适应中值滤波方法和灰度化直方图均衡方法对图像进行预处理,用尺度不变特征变换(SIFT)算法提取视频图像中的特征点,利用仿射模型求解运动参数,采用Kalman滤波对视频图像中的正常扫描进行滤波,最后用相邻帧补偿方法将图像的前一帧作为参考帧对当前帧进行参数补偿,实现高速运动目标的视频图像电子稳像处理。仿真实验表明,新算法能在保留图像中的特征的同时去除图像中含有的抖动,非常适合高速运动视频图像的电子稳像处理,精度提高,计算量明显减少。     

逐步求精的多视角点云配准方法

针对多视角点云配准问题,本文设计了一个合理的目标函数,便于将多视角配准问题分解成多个双视角配准问题,并考虑了两个要素:1）各帧点云均具有其他所有点云所未覆盖的区域;2）基准帧点云的重要程度高于其他点云.为了求解该目标函数,本文提出了逐步求精的解决策略:根据给定的配准初值构造初始模型,依次取出基准帧以外的每帧点云,利用所提出的双视角配准算法计算该帧点云的配准参数,并修正模型,以便进一步计算后续点云的配准参数.遍历完全部点云构成一次完整的循环,多次循环后可获得精确的多视角配准结果.公开数据集上的实验结果表明,本文所提出的方法能够精确、可靠地实现多视角点云配准.     

基于改进SURF的实时视频拼接方法

视频拼接在现实生活中具有十分重要的意义。文中实现的功能是实时地将多路视频进行拼接以形成一个大视野的视频。为了使系统具有良好的实时性,先用DirectShow分离视频流、提取图像帧,然后提出了一种改进的SURF算法进行图像配准,最后采用渐入渐出法进行图像融合。改进的算法在特征点提取时对范围作了限制,同时改进了原有特征描述符,这样可以大大加快提取速度。在特征匹配时采用基于最近邻和次近邻距离比值的方法进行粗匹配,然后采用RANSAC方法进行提纯和求取变换矩阵,最后在视频拼接中,不是对每一帧图像进行配准和融合,而是只对首帧图像进行图像配准,之后的每帧图像利用第一帧计算出的变换矩阵进行图像拼接,这样可以加快视频拼接的速度。实验结果表明,该方法可以实时地生成无缝清晰的宽场景视频。     

结合对齐度准则的视频人脸快速配准算法

在视频人脸替换过程中,针对人脸检测和特征点定位不考虑前后帧的时域联系,导致处理后的视频人脸出现抖动、错位现象的问题,提出结合对齐度准则的视频序列人脸配准方法。通过人脸特征点定位确定待匹配人脸的候选区域和参考人脸五官图像,并根据梯度信息得到参考人脸搜索子图像。构造图像尺度金字塔,在尺度金字塔中快速搜索对齐度极值,极值坐标对应搜索子图像相似度最高的配准点,构造匹配点对。采用Procrustes analysis算法计算对应匹配点的变换关系矩阵,完成待配准图像的坐标变换。根据实验数据,提出的算法提高了优化搜索的速度,处理后的视频与其他算法相比,前后帧对齐的人脸位置和姿态稳定,经处理的视频播放过程中人脸位置自然流畅。算法消除了肤色区域的影响,采用前后帧人脸的有效信息,保持了灰度变化大的人脸区域的位置和姿态,实现视频人脸的精确配准。     

回归模型中变点识别的一种稳健方法

变点识别是数据分析中一个非常重要的研究内容.文中针对目前变点识别研究中忽略了方法的稳健性,未能充分考虑异常值的影响的不足,提出利用一种高度稳健的回归类混合分解算法来识别变点.该方法从混合回归模型的角度,将含有变点的回归模型看作回归类的混合,通过逐步挖掘数据集中的回归类,并对排序后的回归类进行分析,进而确定变点的位置及个数.数值模拟表明,在识别变点的过程中无须预先指定变点的数目,并且具有高度的稳健性和有效性.     

Detection of linear and circular shapes in image analysis

A two-step algorithm is proposed for estimating linear and circular shapes in noisy images. Initially and based on a previously proposed method, the pixels which are close to the edges of the shape are detected. These edges are assumed to be coming from a mixture of (linear or circular) regression functions and the parameters of these functions are estimated. An example with a triangle demonstrates the immense advantage of using an outlier robust estimator for the edge points. A second example deals with a problem from biology where the detection of circular shapes of fungi colonies is of interest.     

基于分集技术的抗线性共谋攻击的视频水印方案

针对视频水印中存在的线性共谋攻击问题,通过分析线性共谋攻击的数学模型,基于通信分集技术建立了一个有效抵抗线性共谋攻击的视频水印设计框架,并根据该框架提出了一种新的能够有效抵抗线性共谋攻击的空域自适应视频水印方案。理论分析和实验结果表明,该方案不仅对剪切、帧去除、帧插入、帧重组以及不可察觉的几何变形具有很好的鲁棒性,而且能够有效地抵抗线性共谋攻击,同时对H.264压缩也具有较好的鲁棒性。     

Tree-structured model diagnostics for linear regression

This paper studies model diagnostics for linear regression models. We propose two tree-based procedures to check the adequacy of linear functional form and the appropriateness of homoscedasticity, respectively. The proposed tree methods not only facilitate a natural assessment of the linear model, but also automatically provide clues for amending deficiencies. We explore and illustrate their uses via both Monte Carlo studies and real data examples.     

Neural network-based light attenuation model for monitoring seagrass population in the Indian river lagoon

Seagrasses have been considered one of the most critical marine habitat types of coastal and estuarine ecosystems such as the Indian River Lagoon. They are an important part of biological productivity, nutrient cycling, habitat stabilization and species diversity and are the primary focus of restoration efforts in the Indian River Lagoon. The areal extent of seagrasses has declined within segments of the lagoon over the years. Light availability to seagrasses is a major criterion limiting their distribution. Decreased water clarity and resulting reduced light penetration have been cited as the major factors responsible for the decline in seagrasses in the lagoon. Hence, light is a critical factor for the survival of seagrass species. Light attenuation coefficient is an important parameter that indicates the light attenuated by the water column and can therefore be used as an indicator of seagrass vigor. A number of region-specific linear light attenuation models have been proposed in the literature. Though, in practice, linear light attenuation models have been commonly used, there is need for a flexible and robust model that incorporates the non-linearities present in coastal and estuarine environments. This paper presents a neural network based model to estimate light attenuation coefficient from water quality parameters and thereby indirectly monitor seagrass population in the Indian River Lagoon. The proposed neural network models were compared with linear regression models, step-wise linear regression models, model trees and support vector machines. The neural network models performed fairly better compared to the other models considered.     

基于支持向量机的股市预测

针对股票市场高燥声、强非线性和不确定性等特点和以往传统神经网络预测方法存在的不足,提出了一种基于支持向量机的股市预测方法。该方法主要运用了支持向量机回归的方法结合滚动时间窗来学习建摸。首先通过把低维输入空间的输入向量映射到高维特征空间,将非线性问题转化为线性,然后在结构风险最小化原则下进行二次规划,并求得最优解,从而建立模型。从仿真实验中可以看到,该方法建立的模型较为准确地预测了600009、000815两只股票的日均价,表现出了较强的泛化能力。     

Centre and Range method for fitting a linear regression model to symbolic interval data

This paper introduces a new approach to fitting a linear regression model to symbolic interval data. Each example of the learning set is described by a feature vector, for which each feature value is an interval. The new method fits a linear regression model on the mid-points and ranges of the interval values assumed by the variables in the learning set. The prediction of the lower and upper bounds of the interval value of the dependent variable is accomplished from its mid-point and range, which are estimated from the fitted linear regression model applied to the mid-point and range of each interval value of the independent variables. The assessment of the proposed prediction method is based on the estimation of the average behaviour of both the root mean square error and the square of the correlation coefficient in the framework of a Monte Carlo experiment. Finally, the approaches presented in this paper are applied to a real data set and their performance is compared.     

Censored depth quantiles

Quantile regression is a wide spread regression technique which allows to model the entire conditional distribution of the response variable. A natural extension to the case of censored observations has been introduced using a reweighting scheme based on the Kaplan-Meier estimator. The same ideas can be applied to depth quantiles. This leads to regression quantiles for censored data which are robust to both outliers in the predictor and the response variable. For their computation, a fast algorithm over a grid of quantile values is proposed. The robustness of the method is shown in a simulation study and on two real data examples.     

Network measurement based modeling and optimization for IP geolocation

IP geolocation plays a critical role in location-aware network services and network security applications. Commercially deployed IP geolocation databases may provide outdated or incorrect location of Internet hosts due to slow record updates and dynamic IP address assignment by the ISPs. Measurement-based IP geolocation is used to provide real time location estimation of Internet hosts based on network delays. This paper proposes a measurement-based IP geolocation framework that provides location estimation of an Internet host in real time. The proposed frame work models the relationship between measured network delays and geographic distances using segmented polynomial regression model and semidefinite programming for optimization. Weighted and non-weighted schemes are evaluated for location estimation. The proposed framework shows close to 17 and 26 miles median estimation error for nodes in North America and Europe, respectively. The proposed schemes achieve 70-80% improvement in median estimation error comparing to the first order regression approach for experimental data collected from Planet-Lab.     

Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis

In this paper, under a semiparametric partly linear regression model with fixed design, we introduce a family of robust procedures to select the bandwidth parameter. The robust plug-in proposal is based on nonparametric robust estimates of the νth derivatives and under mild conditions, it converges to the optimal bandwidth. A robust cross-validation bandwidth is also considered and the performance of the different proposals is compared through a Monte Carlo study. We define an empirical influence measure for data-driven bandwidth selectors and, through it, we study the sensitivity of the data-driven bandwidth selectors. It appears that the robust selector compares favorably to its classical competitor, despite the need to select a pilot bandwidth when considering plug-in bandwidths. Moreover, the plug-in procedure seems to be less sensitive than the cross-validation in particular, when introducing several outliers. When combined with the three-step procedure proposed by Bianco and Boente [2004. Robust estimators in semiparametric partly linear regression models. J. Statist. Plann. Inference 122, 229-252] the robust selectors lead to robust data-driven estimates of both the regression function and the regression parameter.