基于噪声与阴影抑制多模态背景模型的运动物体检测

针对场景照明变化、模型初始化以及阴影等问题, 提出了一种用于视频监视系统运动物体检测的统计多模态背景模型. 通过相隔固定的帧差值阈值化得到背景样本值, 并采用高斯核密度估计方法计算背景灰度的概率密度函数. 利用像素的邻域信息来去除由于摄像机抖动和场景小运动产生的噪声. HMMD色彩信息用来检测和抑制运动投射阴影. 实验结果验证了算法在交通监控前景物体分割中的有效性.     

Brief Research Report: Growth Models With Small Samples and Missing Data

Small samples are common in growth models due to financial and logistical difficulties of following people longitudinally. For similar reasons, longitudinal studies often contain missing data. Though full information maximum likelihood (FIML) is popular to accommodate missing data, the limited number of studies in this area have found that FIML tends to perform poorly with small-sample growth models. This report demonstrates that the fault lies not with how FIML accommodates missingness but rather with maximum likelihood estimation itself. We discuss how the less popular restricted likelihood form of FIML, along with small-sample-appropriate methods, yields trustworthy estimates for growth models with small samples and missing data. That is, previously reported small sample issues with FIML are attributable to finite sample bias of maximum likelihood estimation not direct likelihood. Estimation issues pertinent to joint multiple imputation and predictive mean matching are also included and discussed.     

在非重叠视域监控网络中的人体目标跟踪(英文)

针对存在非重叠视野的摄像机监控网络,提出了一种基于人体外观模型和摄像机间时空信息的人体目标自适应跟踪算法. 对于人体外观模型,首先根据人体测量学理论将人体目标划分成头、躯干和腿 3 个部分,分别提取各部分的 HSV 颜色直方图特征用于构建人体外观模型,然后引入加权因子计算人体目标之间的相似度,最后采用一种基于双阈值的相似度排序算法确定人体目标的匹配关系. 对于摄像机间的时空信息,通过增量学习,不断积累目标关联信息,经统计分析逐步更新摄像机间时空信息. 实验结果验证了所提出的跟踪算法在无需摄像机标定的条件下能够实现人体目标的连续跟踪,且随着关联匹配信息的累加,算法的跟踪准确性也逐步提高.     

The Impact of Moderate Priors For Bayesian Estimation and Testing of Item Factor Analysis Models When Maximum Likelihood is Unsuitable

In psychological research, available data are often insufficient to estimate item factor analysis (IFA) models using traditional estimation methods, such as maximum likelihood (ML) or limited information estimators. Bayesian estimation with common-sense, moderately informative priors can greatly improve efficiency of parameter estimates and stabilize estimation. There are a variety of methods available to evaluate model fit in a Bayesian framework; however, past work investigating Bayesian model fit assessment for IFA models has assumed flat priors, which have no advantage over ML in limited data settings. In this paper, we evaluated the impact of moderately informative priors on ability to detect model misfit for several candidate indices: posterior predictive checks based on the observed score distribution, leave-one-out cross-validation, and widely available information criterion (WAIC). We found that although Bayesian estimation with moderately informative priors is an excellent aid for estimating challenging IFA models, methods for testing model fit in these circumstances are inadequate.     

Handling Missing Data in the Modeling of Intensive Longitudinal Data

Myriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate time-series models under different missing data mechanisms. They include a full MI approach, in which all dependent variables and covariates are imputed simultaneously, and a partial MI approach, in which missing covariates are imputed with MI, whereas missingness in the dependent variables is handled via full information maximum likelihood estimation. We found that under correctly specified models, partial MI produces the best overall estimation results. We discuss the strengths and limitations of the two MI approaches, and demonstrate their use with an empirical data set in which children’s influences on parental conflicts are modeled as covariates over the course of 15 days (Schermerhorn, Chow, & Cummings, 2010).     

稀疏过程在一类相依多险种风险模型中的应用

将汽车保险中的一类相依两险种风险模型扩展到相依三险种风险模型，用对齐次poisson过程的稀疏与分解将该模型转化为古典风险模型，并证明了转化的合理性，进而给出破产概率的一般表达式及其一个上界估计．这种转化的方法对类似的多险种相依情形同样适用．     

视频多目标遮挡中人体目标跟踪

提出一种视频多目标中跟踪人体目标的有效方法．在单摄像头对固定场景监控中,首先通过彩色空间的降维,根据置信度更新背景模型,消除时间变化引起的阳光,天气等因素对背景图像的影响;然后使用背景减除获得多目标模板,分析多目标遮挡问题,利用人体目标的多子块灰度特征匹配得多目标遮挡中人体目标的跟踪;实验证明这种方法可有效地用于视频的人体跟踪中．     

一种基于二维图像信息的三维地形测量

研究目的：利用数字图像测量技术对河流模型实验中的河床地形测晕研究。创新要点：以高质量的图像径向畸变校正为基础,依据多幅图像间 映射换算以及单幅图像灰度信息变换两方面的研究,来提取二维地形信息。研究方法：使用考虑残差的一阶误差传播的最大似然估计方法求解图像与地形坐标的映射变换参数;借助漫反射光照模型,通过适当假设,推导出地形高程与图像灰度、灰度梯度及太阳办向的关系式。重要结论：1．以Devernay基于直线的图像畸变校正方法为基础,通过使用更高精度的Canny边缘检测算子提取图像边缘,黄金分割法和二次插值法相结合的最优化方法求取畸变系数等,可以得到较好的校正效果,求算的畸变系数精度更高,求算过程更快;2．利用图像与地面间的透视投影关系方法求解地形高程小标,均匀并尽可能多地将控制点布置住控制区域内,使计算值更接近实测值,并满足河流演变实验的精度要求;3．通过推导关系式,理论上可以根据图像灰度、灰度梯度、太阳方向向晕算得地形高程。     

Developing rigid constraint for the estimation of pose and structure from a single image

INTRODUCTION Pose and structural parameters analysis of 2Dimages sets find application in many areas such asobject recognition, motion estimation, navigationplanning, and structural analysis of 3D objects(Huang and Netravali, 1994), image communica-tion, and image coding (Mitiche and Aggarwal,1986). Normally, given one set of 3D object pointdata and their corresponding 2D projective imagepoint data, the 3D-2D calibration problem is re-ferred to as the estimation of the camera par…     

Developing rigid constraint for the estimation of pose and structure from a single image

Pose and structure estimation from a single image is a fundamental problem in machine vision and multiple sensor fusion and integration.In this paper we propose using rigid constraints described in different coordinate frames to iteratively estimate structural and camera pose parameters.Using geometric properties of reflected correspondences we put forward a new concept,the reflected pole of a rigid transformation.The reflected pole represents a general analysis of transformations that can be applied to both 2D and 3D transformations.We demonstrate how the concept is applied to calibration by proposing an iterative method to estimate the structural parameters of objects.The method is based on a coarse-to-fine strategy in which initial estimation is obtained through a classical linear algorithm which is then refined by iteration.For a comparative study of performance,we also implemented an extended motion estimation algorithm(from 2D-2D to 3D-2D case)based on epipolar geometry.     

Embedding ensemble tracking in a stochastic framework for robust object tracking

We propose an algorithm of embedding ensemble tracking in a stochastic framework to achieve robust tracking performance under partial occlusion, illumination changes, and abrupt motion. It operates on likelihood images generated by the ensemble method, and combines mean shift and particle filtering in a principled way, where a better proposal distribution is designed by first propagating particles via a motion model, and then running mean shift to move towards their local peaks in the likelihood image. An observation model in the particle filter incorporates global and local information within a region, and an adaptive motion model is adopted to depict the evolution of the object state. The algorithm needs fewer particles to manage the tracking task compared with the general particle filter, and recaptures the object quickly after occlusion occurs. Experiments on two image sequences demonstrate the effectiveness and robustness of the proposed algorithm.     

Relighting multiple color textures

INTRODUCTION When constructing complete 3D models, multi- ple color texture maps are often necessary. Suppose that the object of interest is fixed and images of it are acquired by one or more cameras from different po- sitions and directions. The viewpoints and the illu- mination may be different for each acquisition. The geometric model of the object may come from merging of several 3D models. The 2D-3D registra-tion between the textures and the geometric model is done by a previous st…     

A Comparison of Estimation Techniques for IRT Models With Small Samples

The usefulness of item response theory (IRT) models depends, in large part, on the accuracy of item and person parameter estimates. For the standard 3 parameter logistic model, for example, these parameters include the item parameters of difficulty, discrimination, and pseudo-chance, as well as the person ability parameter. Several factors impact traditional marginal maximum likelihood (ML) estimation of IRT model parameters, including sample size, with smaller samples generally being associated with lower parameter estimation accuracy, and inflated standard errors for the estimates. Given this deleterious impact of small samples on IRT model performance, use of these techniques with low-incidence populations, where it might prove to be particularly useful, estimation becomes difficult, especially with more complex models. Recently, a Pairwise estimation method for Rasch model parameters has been suggested for use with missing data, and may also hold promise for parameter estimation with small samples. This simulation study compared item difficulty parameter estimation accuracy of ML with the Pairwise approach to ascertain the benefits of this latter method. The results support the use of the Pairwise method with small samples, particularly for obtaining item location estimates.     

Framework of a Decision-Theoretic Tutoring System for Learning of Mechanics

This paper presents the application of decision-theoretic technique to computer-based tutoring system for elementary mechanics. The technique uses sound probabilistic reasoning and a student model to identify learner's misconception(s). Bayesian belief networks are the building blocks of the student model. The probability values in Bayes' nets are provided by teacher and are based on her judgement, but may be substituted with actual statistics. Evidence on student's mastery of concepts is obtained through her responses to appropriately selected items. Subsequently, Rasch one-parameter model is used to calibrate the item and person parameters (also known as difficulty and ability indices, respectively). The system is able to provide teacher with information for fine-tuning her pedagogical instructions and guide her in coaching students. It is also able to provide students with immediate feedback to improve their proficiencies and ultimately their grades.     

基于块匹配的电子图像稳定算法

针对摄像机不稳定引起的图像序列抖动提出一种基于块匹配的全局运动估计与补偿算法,介绍了它在电子图像稳定(EIS)系统中的应用。该算法较好地解决了摄像机图像在水平与垂直方向的抖动问题。     

Parameter Recovery and Classification Accuracy Under Conditions of Testlet Dependency: A Comparison of the Traditional 2PL,Testlet, and Bi-Factor Models

ABSTRACT

Testlets, or groups of related items, are commonly included in educational assessments due to their many logistical and conceptual advantages. Despite their advantages, testlets introduce complications into the theory and practice of educational measurement. Responses to items within a testlet tend to be correlated even after controlling for latent ability, which violates the assumption of conditional independence made by traditional item response theory models. The present study used Monte Carlo simulation methods to evaluate the effects of testlet dependency on item and person parameter recovery and classification accuracy. Three calibration models were examined, including the traditional 2PL model with marginal maximum likelihood estimation, a testlet model with Bayesian estimation, and a bi-factor model with limited-information weighted least squares mean and variance adjusted estimation. Across testlet conditions, parameter types, and outcome criteria, the Bayesian testlet model outperformed, or performed equivalently to, the other approaches.     

出生人口性别比的一种估计模型及其精度分析

本文将出生人口性别比的探讨化归为概率中的摸球模型,据其求出出生人口性别比的极大似然估计,并分析估计的精度.构造L-M图用于出生人口性别比的动态监测.     

Unwrapping and stereo rectification for omnidirectional images

Omnidirectional imaging sensors have been used in more and more applications when a very large field of view is required. In this paper, we investigate the unwrapping, epipolar geometry and stereo rectification issues for omnidirectional vision when the particular mirror model and the camera parameters are unknown in priori. First, the omnidirectional camera is calibrated under the Taylor model, and the parameters related to this model are obtained. In order to make the classical computer vision algorithms of conventional perspective cameras applicable, the ring omnidirectional image is unwrapped into two kinds of panoramas： cylinder and cuboid. Then the epipolar geometry of arbitrary camera configuration is analyzed and the essential matrix is deduced with its properties being indicated for ring images. After that, a simple stereo rectification method based on the essential matrix and the conformal mapping is proposed. Simulations and real data experimental results illustrate that our methods are effective for the omnidirectional camera under the constraint of a single view point.     

教育中的榜样:伦理的视角

对教育中榜样的研究,涉及事实与价值两个层面.以往大多基于事实层面进行讨论,而忽视了相关价值问题的探讨.事实上,榜样因具有符合主体价值取向的优秀品质而被选取为学习的目标.在教育中,他们可以将品质具体化并激励学生的成长.以关注"我应该成为什么样的人"为中心的美德伦理学启发我们,为有效地发挥榜样的教育意义,应以"成为榜样那样的人"为目标,注重人的品质与理性,选取涵盖履行了责任的主体与践行超责任的主体两种人为榜样.具体讲:了解学生"想成为的人",建立"榜样库"供其自主选择;重视学生的理解与反思;提供践行机会;注重"品质中心"的评价与反馈.