一种基于特征自动选取的跟踪算法

在复杂的环境下,基于单一特征的视觉跟踪算法,往往不能满足实际环境的需要,对此文中提出了一种基于特征自动选取的跟踪方法。考虑到不同特征在不同场景下对目标与背景的区分能力的不同,重新定义了适合跟踪的背景区域,并在此基础上提出了特征自动选取的策略。基于此策略,利用目标的颜色特征和纹理特征,具体实现了特征自动选取的跟踪算法。实验结果证明了该策略的可行性。     

基于全变分模型的图像噪声去除效果比较

基于偏微分方程的图像去噪方法由于将数学与工程结合得更加紧密,具有较强的自适应能力和灵活性.本文首先介绍了目前已经提出的变分模型的快速Split-Bregman算法,然后通过大量数值实验对不同模型的去噪效果进行了比较.所研究的模型包括L1范数、L2范数、LTV(1ayered total variation)规则项、MTV(multicharmel total variation)规则项和CTV(color total variation)规则项,从灰度图像和彩色多通道图像两方面进行分析.实验结果表明对于灰度图像基于L1范数的TV去噪模型效果较好,彩色图像中CTV模型对图像去噪边缘保持最好,其他依次是MTV模型、LTV模型.     

Linear model validation and order selection using higher orderstatistics

There exists several methods for fitting linear models to linear stationary nonGaussian signals using higher order statistics. The models are fitted under certain assumptions on the data and the underlying (true) model. This paper is devoted to the problem of model validation, i.e., to checking if the fitted linear model is consistent with the underlying basic assumptions. Model order selection is a by-product of the solution. We provide a fairly easy-to-apply statistical test based on the asymptotic properties of the bispectrum of the inverse filtered data. Computer simulation results are presented for both linear model validation and model order selection. The proposed model order selection approach is compared with an existing order selection method based upon rank testing via singular value decomposition     

On color transforms and bit allocation for optimal subband image compression

Although subband transform coding is a useful approach to image compression and communication, the performance of this method has not been analyzed so far for color images, especially when the selection of color components is considered. Obviously, the RGB components are not suitable for such a compression method due to their high inter-color correlation. On the other hand, the common selection of YUV or YIQ is rather arbitrary and in most cases not optimal. In this work we introduce a rate–distortion model for color image compression and employ it to find the optimal color components and optimal bit allocation (optimal rates) for the compression. We show that the DCT (discrete cosine transform) can be used to transform the RGB components into an efficient set of color components suitable for subband coding. The optimal rates can be also used to design adaptive quantization tables in the coding stage with results superior to fixed quantization tables. Based on the presented results, our conclusion is that the new approach can improve presently available methods for color image compression and communication.     

3D model retrieval by sample based alignment

In 3D model retrieval, preprocessing of 3D models is needed, in which alignment is a key factor that significantly affects retrieval performance. In particular, the anti-rotation image feature can obtain the alignment effect of 3D model views. In practice, the focus of many users of 3D models is not just on retrieval performance, but the use of aligned models for different purposes. In this paper, we propose a method, namely Sample Based Alignment (SBA) for better 3D model alignment and retrieval. In SBA, given a class, a sample model is used as the target for alignment, after which each 3D model in this class is then aligned one by one, i.e., the 3D model is actually rotated. Our experimental results, based on two 3D model datasets and performance comparisons with other methods, demonstrate the superiority of the SBA method over state-of-the-art methods in terms of 3D model retrieval and classification.     

基于HSV的乳腺癌细胞识别方法

乳腺癌是女性致死率最高的疾病之一,严重威胁女性的身体健康。目前,病理医生判断病症等级,仍通过观察病理切片中的增殖细胞个数,具有很大的主观性。本文提出了一种将RGB彩色空间和HSV彩色空间模型结合的方法,采用各分量的色度特征,通过量化分析建立色度特征方程,实现从病理图像中检测识别乳腺癌增殖细胞。经20组乳腺癌病理切片图像验证,准确性达到了94.2%,优于单色彩空间模型方法约4%,本文提出方法能够更精确识别乳腺癌细胞,为乳腺癌细胞识别提供了一种新方法。     

A practical method for obtaining prior distributions in reliability

In this paper, we propose a comprehensive methodology to specify prior distributions for commonly used models in reliability. The methodology is based on characteristics easy to communicate by the user in terms of time to failure. This information could be in the form of intervals for the mean and standard deviation, or quantiles for the failure-time distribution. The derivation of the prior distribution is done for two families of proper initial distributions, namely s-normal-gamma, and uniform distribution. We show the implementation of the proposed method to the parameters of the s-normal, lognormal, extreme value, Weibull, and exponential models. Then we show the application of the procedure to two examples appearing in the reliability literature, and . By estimating the prior predictive density, we find that the proposed method renders consistent distributions for the different models that fulfill the required characteristics for the time to failure. This feature is particularly important in the application of the Bayesian approach to different inference problems in reliability, model selection being an important example. The method is general, and hence it may be extended to other models not mentioned in this paper.     

Explanation for the dark I–V curve of III–V concentrator solar cells

The measurement of the dark I–V curve is one of the most straightforward methods for characterizing solar cells. Consequently, an accurate knowledge of its meaning is of high relevance for the comprehension and technological feedback of these devices. In this paper, an explanation of the dark I–V curve for concentrator III–V solar cells is presented using a 3D (three‐dimensional) model in order to provide a proper data fit that provides meaningful physical parameters that are also compatible and coherent with a data fit from illumination curves. The influence on the dark I–V curve of the most significant series resistance components of concentrator solar cells is also analysed concluding that only the vertical component as well as the front contact‐specific resistance can be assessable by means of this characterization method while both emitter and metal sheet resistances cannot be detected. For comparison purposes, the same experimental data have been fitted by means of a traditional two‐diode model showing that, although an accurate dark I–V curve fitting can be achieved, the extracted parameters are unable to reproduce illumination data since lumped models assume the same ohmic losses distribution for both dark and illumination conditions. Copyright © 2007 John Wiley & Sons, Ltd.     

联合人体模型与块生长的人群目标分割

人群目标的准确分割,是进行多相机联合目标跟踪与识别的关键。该文首先构造包含位置、尺寸、姿态信息的人体粗略姿态模型,并利用贝叶斯模型获得对应的目标模型。然后将前景区域利用分水岭算法划分为颜色分布一致的子块,通过位置和颜色约束解决遮挡目标的种子块选取问题。最后通过块生长获得每个目标区域。对于颜色相似子块,通过比较其产生的边缘能量确定其所属目标。实验结果表明,本文能够实现对人群目标较精确分割,且对背景噪声具有一定的抗干扰能力。     

Total Variation Denoising and Enhancement of Color Images Based on the CB and HSV Color Models

Most denoising and enhancement methods for color images have been formulated on linear color models, namely, the channel-by-channel model and vectorial model. In this paper, we study the total variation (TV) restoration based on the two nonlinear (or nonflat) color models: the chromaticity–brightness model and hue–saturation–value model. These models are known to be closer to human perception. Recent works on the variational/PDE method for nonflat features by several authors enable us to denoise the chromaticity and hue components directly. We present both the mathematical theory and digital implementation for the TV method. Comparison to the traditional TV restorations based on linear color models is made through various experiments.     

Super peer selection strategy in peer‐to‐peer networks based on learning automata

A super peer is a peer that has the highest capacity in comparison with other peers in the network. It is trying to reduce the load of the rest of the peers and improve network performance. Selecting a super peer in a peer‐to‐peer–based network is a very crucial challenge. As the ability of peers are very different, by considering capacity of each peer and selecting a proper role, we can use network components much more efficiently. Because of the dynamicity of these networks, comparative methods of selecting super peers is of special importance. Comparative selection is continuously trying to select proper super peer. In recent studies, learning automata was introduced as a powerful learning model to solve this issue. In most of the studies, learning automata with an S model is employed. In this article, another selection method of learning automata with a P model environment is presented and its capability for super peer selection is shown. Moreover, simulation results show that removing some of the super peers would result in better performance in terms of inversion time in the high level of super‐peer capacity, required time for selecting proper super peer, and super peer tolerance.     

A New Approach to Variable Selection Using the TLS Approach

The problem of variable selection is one of the most important model selection problems in statistical applications. It is also known as the subset selection problem and arises when one wants to explain the observations or data adequately by a subset of possible explanatory variables. The objective is to identify factors of importance and to include only variables that contribute significantly to the reduction of the prediction error. Numerous selection procedures have been proposed in the classical multiple linear regression model. We extend one of the most popular methods developed in this context, the backward selection procedure, to a more general class of models. In the basic linear regression model, errors are present on the observations only, if errors are present on the regressors as well, one gets the errors-in-variables model which for Gaussian noise becomes the total-least-squares (TLS) model, this is the context considered here     

A system for the automatic selection of conspicuous color sets forqualitative data display

The authors describe the main features of a system supporting the selection of color palettes for qualitative data representation, such as in supervised or unsupervised image classification. Based on visual interaction, the system provides effective tools for browsing the Munsell color space and setting perceptual constraints on the colors, which it then selects automatically. The system is now available for academic and nonprofit purposes     

Sharing spray and wait routing algorithm in opportunistic networks

Due to the instability and intermittent connectivity of links among the nodes and the lack of connectivity in opportunistic network, it is not feasible to use common routing for delivering messages. The only practical method for routing and delivering messages is to use the store-carry-forward routing method. As a case in point, spray and wait is considered to be one of the most appropriate routing methods. The efficiency of this method depends directly on the proper selection of the next hop and the number of copies when it encounters a node. In this paper, a method was proposed that constantly selects the next node and considers the number of copies a node can deliver. In the proposed method, the selection of the next node and the number of message copies to be transmitted by the next hop are based on message carrying time and the probability of message delivery. The network model, based on Markov chain, is extended for analysis. Simulation and analysis results showed that significant enhancement is obtained with the proposed method when measuring metrics such as delay, delivery ratio and copy do comparisons with similar methods.     

The Research and Application of ATM Testing

The Research and Application of ATM Testing     

Transition management-a structured perspective

The various major models of transition management are presented and compared. They are organized into three distinct categories, based on their prescribed transition increment size, management structure, or method for influencing change in employees. Factors affecting the choice of a model are also provided. Through the careful selection of an appropriate method from each category, it is proposed that a hybrid transition structure has a better chance of success than the utilization of any one method     

长期视频监控系统的多分布模型背景差方法

提出在长期视频监控系统中采用背景差进行运动目标提取时算法所要满足的基本要求，并提出了一种能够满足这些要求的背景差方法。该方法用色度、亮度窨的多个分布模型来建立背景模型，描述彩色视频图像的背景像素点及其统计特性，在对背景模型更新的时将均值、方差的更新速率和多个模型的更替速率分开。对像素值属于多个分布模型的情况，用最小相似距离确定更新的模型。该方法利用提取的前景像素点信息反馈以检测光强的突变，利用亮度信息消除运动目标的阴影。实验证明该方法很好的满足了长期视监控系统的要求。     

（英）集成颜色特征和统计特征的极化SAR图像城区分类框架

在传统的利用极化合成孔径雷达(PolSAR)遥感图像分类中,除了近期一个有监督分类的工作,很少涉及颜色特征。与该工作不同,在本文中,针对城区分类,利用颜色特征构造一个新颖的无监督的分类框架。首先,基于最近提出的PolSAR数据的四分量分解模型,计算了常用的颜色空间：YUV,RGB,HSI和CIELab,通过引入颜色熵量化的选择颜色特征,然后,联合纹理特征和扩展的散射功率熵,用自适应的均值漂移算法分割PolSAR图像,最后,根据基于G0分布的距离测度合并聚簇为较为匀质的地物类别。通过L波段AIRSAR数据和C波段Radarsat-2的PolSAR数据验证了提出算法的有效性,分类正确率表明,相比于已有的工作,提出的算法对于城区有较好的区分能力。     

Color image watermark decoder by modeling quaternion polar harmonic transform with BKF distribution

It still remains a challenging problem to improve both of the imperceptibility and robustness for the digital image watermarking task. In this study, a color image watermarking scheme is proposed based on quaternion polar harmonic transform (QPHT) with maximum likelihood decoder, which better balances imperceptibility and robustness in a novel fashion. Thanks to the strong robustness of QPHT, the digital watermark is embedded into the QPHT magnitudes using a multiplicative approach. At the receiver, the watermark decoder can be formulated as a problem of weak signal detection, as the QPHT magnitudes are highly non-Gaussian. In view of this, selection of proper models is of great importance to construct a suitable watermark decoder. Further, we validate that the probability density function of Bessel K form suitably models the heavy tail behavior of QPHT magnitudes, and thus the maximum likelihood decoder is developed by modeling QPHT magnitudes with Bessel K form distribution. Experimental results on standard benchmarks demonstrate that the proposed approach provides better imperceptibility and robustness against various kinds of attacks compared to previous state-of-the-art methods.