Identifying virus-cell fusion in two-channel fluorescence microscopy image sequences based on a layered probabilistic approach

The entry process of virus particles into cells is decisive for infection. In this work, we investigate fusion of virus particles with the cell membrane via time-lapse fluorescence microscopy. To automatically identify fusion for single particles based on their intensity over time, we have developed a layered probabilistic approach. The approach decomposes the action of a single particle into three abstractions: the intensity over time, the underlying temporal intensity model, as well as a high level behavior. Each abstraction corresponds to a layer and these layers are represented via stochastic hybrid systems and hidden Markov models. We use a maxbelief strategy to efficiently combine both representations. To compute estimates for the abstractions we use a hybrid particle filter and the Viterbi algorithm. Based on synthetic image sequences, we characterize the performance of the approach as a function of the image noise. We also characterize the performance as a function of the tracking error. We have also successfully applied the approach to real image sequences displaying pseudotyped HIV-1 particles in contact with host cells and compared the experimental results with ground truth obtained by manual analysis.     

Visual tracking in high-dimensional state space by appearance-guided particle filtering

In this paper, we propose a new approach, appearance-guided particle filtering (AGPF), for high degree-of-freedom visual tracking from an image sequence. This method adopts some known attractors in the state space and integrates both appearance and motion-transition information for visual tracking. A probability propagation model based on these two types of information is derived from a Bayesian formulation, and a particle filtering framework is developed to realize it. Experimental results demonstrate that the proposed method is effective for high degree-of-freedom visual tracking problems, such as articulated hand tracking and lip-contour tracking.     

Target Tracking by Particle Filtering in Binary Sensor Networks

We present particle filtering algorithms for tracking a single target using data from binary sensors. The sensors transmit signals that identify them to a central unit if the target is in their neighborhood; otherwise they do not transmit anything. The central unit uses a model for the target movement in the sensor field and estimates the target's trajectory, velocity, and power using the received data. We propose and implement the tracking by employing auxiliary particle filtering and cost-reference particle filtering. Unlike auxiliary particle filtering, cost-reference particle filtering does not rely on any probabilistic assumptions about the dynamic system. In the paper, we also extend the method to include estimation of constant parameters, and we derive the posterior Cramer-Rao bounds (PCRBs) for the states. We show the performances of the proposed methods by extensive computer simulations and compare them to the derived bounds.     

基于粒子滤波的运动物体检测跟踪

提出了一种状态空间模型粒子滤波算法,并应用于运动目标的跟踪。该方法基于贝叶斯估计,利用粒子集来表示概率,通过递推的贝叶斯滤波来近似逼近最优化结果,在预设搜索区域用粒子群找到和目标模板最相似的中心位置,并以该位置作为观测值,进行跟踪。仿真实验结果和两种实际条件下效果比较表明该算法在跟踪低常速运动中精准性高,是一种有效的目标跟踪方法。     

Adaptive Rao–Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance

Particle filters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particle filter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particle filtering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particle filter     

Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy

It is important to observe and study cancer cells' cycle progression in order to better understand drug effects on cancer cells. Time-lapse microscopy imaging serves as an important method to measure the cycle progression of individual cells in a large population. Since manual analysis is unreasonably time consuming for the large volumes of time-lapse image data, automated image analysis is proposed. Existing approaches dealing with time-lapse image data are rather limited and often give inaccurate analysis results, especially in segmenting and tracking individual cells in a cell population. In this paper, we present a new approach to segment and track cell nuclei in time-lapse fluorescence image sequence. First, we propose a novel marker-controlled watershed based on mathematical morphology, which can effectively segment clustered cells with less oversegmentation. To further segment undersegmented cells or to merge oversegmented cells, context information among neighboring frames is employed, which is proved to be an effective strategy. Then, we design a tracking method based on modified mean shift algorithm, in which several kernels with adaptive scale, shape, and direction are designed. Finally, we combine mean-shift and Kalman filter to achieve a more robust cell nuclei tracking method than existing ones. Experimental results show that our method can obtain 98.8% segmentation accuracy, 97.4% cell division tracking accuracy, and 97.6% cell tracking accuracy     

Adaptive Rao-Blackwellized particle filter and its evaluation for tracking in surveillance.

Particle filters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particle filter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particle filtering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particle filter.     

Alignment of 3-D optical coherence tomography scans to correct eye movement using a particle filtering

Eye movement artifacts occurring during 3-D optical coherence tomography (OCT) scanning is a well-recognized problem that may adversely affect image analysis and interpretation. A particle filtering algorithm is presented in this paper to correct motion in a 3-D dataset by considering eye movement as a target tracking problem in a dynamic system. The proposed particle filtering algorithm is an independent 3-D alignment approach, which does not rely on any reference image. 3-D OCT data is considered as a dynamic system, while the location of each A-scan is represented by the state space. A particle set is used to approximate the probability density of the state in the dynamic system. The state of the system is updated frame by frame to detect A-scan movement. The proposed method was applied on both simulated data for objective evaluation and experimental data for subjective evaluation. The sensitivity and specificity of the x-movement detection were 98.85% and 99.43%, respectively, in the simulated data. For the experimental data (74 3-D OCT images), all the images were improved after z-alignment, while 81.1% images were improved after x-alignment. The proposed algorithm is an efficient way to align 3-D OCT volume data and correct the eye movement without using references.     

一种改进粒子滤波的双站无源定位跟踪算法

在非线性非高斯状态空间下，粒子滤波器是一种有效的非线性滤波算法，它的关键问题包括粒子权重的计算、粒子重采样和状态估计等。本文根据粒子滤波算法思想和双站无源定位跟踪的非线性，将粒子滤波算法用于双站无源定位跟踪问题，给出了一种改进的粒子滤波算法，并对其关键问题根据双站无源定位跟踪的特殊性进行了改进。利用Matlab进行了仿真实验，与最小二乘算法、扩展卡尔曼滤波算法进行了比较，结果表明所提算法定位跟踪精度优于其他方法。     

基于分层采样粒子滤波的麦克风阵列说话人跟踪方法

针对噪声与混响环境下的说话人跟踪问题,本文提出了一种基于粒子滤波的麦克风阵列声源定位与跟踪方法.该方法在粒子滤波框架下,将无混响影响的语音建立信号作为观测信息,通过计算麦克风阵列波束形成器的输出能量来构建似然函数,同时考虑语音信号不同频率成分在声源定位中的作用,利用分层采样方法提高粒子的采样效率.实验结果表明,本文方法提高了说话人声源跟踪系统的抗噪声与抗混响能力.     

Joint parameter estimation and decoding in a distributed receiver

This paper presents an algorithm for iterative joint channel parameter (carrier phase, Doppler shift, and Doppler rate) estimation and decoding of transmission over channels affected by Doppler shift and Doppler rate using a distributed receiver. This algorithm is derived by applying the sum-product algorithm (SPA) to a factor graph representing the joint a posteriori distribution of the information symbols and channel parameters given the channel output. In this paper, we present two methods for dealing with intractable messages of the SPA. In the first approach, we use particle filtering with sequential importance sampling for the estimation of the unknown parameters. We also propose a method for fine-tuning of particles for improved convergence. In the second approach, we approximate our model with a random walk phase model, followed by a phase tracking algorithm and polynomial regression algorithm to estimate the unknown parameters. We derive the Weighted Bayesian Cramer-Rao Bounds for joint carrier phase, Doppler shift, and Doppler rate estimation, which take into account the prior distribution of the estimation parameters and are accurate lower bounds for all considered signal-to-noise ratio values. Numerical results (of bit error rate and the mean-square error of parameter estimation) suggest that phase tracking with the random walk model slightly outperforms particle filtering. However, particle filtering has a lower computational cost than the random walk model-based method.     

基于粒子滤波的RFID室内节点定位跟踪研究

为了解决复杂室内环境下的RFID动态节点定位跟踪问题,文中建立了动态节点的运动模型和信号测量模型。仿真采用基于信号RSSI定位方法,结合运用等边三角形定位算法。由于室内射频信号具有较高的噪声污染,因此首先对采集的信号运用粒子滤波技术进行滤波处理,然后运用高斯粒子滤波算法对室内移动的RFID进行了定位跟踪预测。仿真结果表明该算法可以有效地对室内动态节点进行定位跟踪,精度较高,稳定性好,结果进一步说明高斯粒子滤波能有效地抑制室内射频噪声。     

Abrupt motion tracking via intensively adaptive Markov-chain Monte Carlo sampling

The robust tracking of abrupt motion is a challenging task in computer vision due to its large motion uncertainty. While various particle filters and conventional Markov-chain Monte Carlo (MCMC) methods have been proposed for visual tracking, these methods often suffer from the well-known local-trap problem or from poor convergence rate. In this paper, we propose a novel sampling-based tracking scheme for the abrupt motion problem in the Bayesian filtering framework. To effectively handle the local-trap problem, we first introduce the stochastic approximation Monte Carlo (SAMC) sampling method into the Bayesian filter tracking framework, in which the filtering distribution is adaptively estimated as the sampling proceeds, and thus, a good approximation to the target distribution is achieved. In addition, we propose a new MCMC sampler with intensive adaptation to further improve the sampling efficiency, which combines a density-grid-based predictive model with the SAMC sampling, to give a proposal adaptation scheme. The proposed method is effective and computationally efficient in addressing the abrupt motion problem. We compare our approach with several alternative tracking algorithms, and extensive experimental results are presented to demonstrate the effectiveness and the efficiency of the proposed method in dealing with various types of abrupt motions.     

Bayesian methods for multiaspect target tracking in image sequences

In this paper, we introduce new algorithms for automatic tracking of multiaspect targets in cluttered image sequences. We depart from the conventional correlation filter/Kalman filter association approach to target tracking and propose instead a nonlinear Bayesian methodology that enables direct tracking from the image sequence incorporating the statistical models for the background clutter, target motion, and target aspect change. Proposed algorithms include 1) a batch hidden Markov model (HMM) smoother and a sequential HMM filter for joint multiframe target detection and tracking and 2) two mixed-state sequential importance sampling trackers based on the sampling/importance resampling (SIR) and the auxiliary particle filtering (APF) techniques. Performance studies show that the proposed algorithms outperform the association of a bank of template correlators and a Kalman filter in adverse scenarios of low target-to-clutter ratio and uncertainty in the true target aspect.     

基于加权子空间拟合的声源定位与跟踪方法

麦克风阵列声源定位可为复杂环境下的说话人空间位置估计问题提供一种有效的解决方案。该文基于粒子滤波框架,提出了一种加权子空间拟合声源定位与跟踪方法。该方法将窄带子空间拟合算法的代价函数推广至宽带情形,构建了一种适用于宽带语音信号的似然函数,并结合说话人的运动模型估计声源的位置。计算机仿真与实测结果验证了该方法的有效性。     

Algorithmic and architectural optimizations for computationally efficient particle filtering.

In this paper, we analyze the computational challenges in implementing particle filtering, especially to video sequences. Particle filtering is a technique used for filtering nonlinear dynamical systems driven by non-Gaussian noise processes. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. In this paper, we analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and, in particular, concentrate on implementations that have minimum processing times. It is shown that the design parameters for the fastest implementation can be chosen by solving a set of convex programs. The proposed computational methodology was verified using a cluster of PCs for the application of visual tracking. We demonstrate a linear speed-up of the algorithm using the methodology proposed in the paper.     

Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics.

We present a new, robust, computational procedure for tracking fluorescent markers in time-lapse microscopy. The algorithm is optimized for finding the time-trajectory of single particles in very noisy dynamic (two- or three-dimensional) image sequences. It proceeds in three steps. First, the images are aligned to compensate for the movement of the biological structure under investigation. Second, the particle's signature is enhanced by applying a Mexican hat filter, which we show to be the optimal detector of a Gaussian-like spot in 1/omega2 noise. Finally, the optimal trajectory of the particle is extracted by applying a dynamic programming optimization procedure. We have used this software, which is implemented as a Java plug-in for the public-domain ImageJ software, to track the movement of chromosomal loci within nuclei of budding yeast cells. Besides reducing trajectory analysis time by several 100-fold, we achieve high reproducibility and accuracy of tracking. The application of the method to yeast chromatin dynamics reveals different classes of constraints on mobility of telomeres, reflecting differences in nuclear envelope association. The generic nature of the software allows application to a variety of similar biological imaging tasks that require the extraction and quantitation of a moving particle's trajectory.     

基于局部粒子密度的粒子滤波跟踪方法

定义了欧氏空间内的局部粒子密度的概念,针对四种不同的情况作出分析,对优选粒子的优劣作出评价并指导后续的粒子重采样和模板更新过程,并在此基础上给出了一种新的基于局部粒子密度的目标跟踪方法.比较实验显示,相对于原始粒子滤波方法和其他粒子重要性重采样方法,该方法在保证跟踪有效性的同时,提高了跟踪效率.     

基于粒子滤波框架联合仿射和外貌模型的目标跟踪

针对视频序列中目标的跟踪问题,提出了一种基于粒子滤波框架的联合仿射和外貌模型的目标跟踪算法.该算法首先提取图像帧之间的相关特征点,通过求解Sylvester方程得到仿射参数,然后将仿射参数嵌入到基于仿射群的粒子滤波框架中进行平滑估计.利用基于仿射群的一阶自回归过程模拟状态的变化,联合仿射特征点模型和外貌模型进行似然估计,得到粒子的最佳平均状态,进而对目标实施跟踪.实验结果表明,在目标经历姿势和尺度变化、遮挡以及复杂背景等情况下,提出的算法能够有效地跟踪目标,较之其他相关算法具有很强的鲁棒性.     

正则化粒子滤波在水下目标跟踪中的应用

针对传统卡尔曼滤波(KF)及扩展卡尔曼滤波(EKF)在非线性目标跟踪模型中,跟踪精度较差的问题,本文给出了一种基于正则化粒子滤波(RPF)的水下目标跟踪算法。文中在一种模拟水下目标跟踪环境的非线性动态模型中对所提出的算法进行了仿真试验,并将其跟踪性能与扩展卡尔曼滤波和标准粒子滤波算法(PF)进行了比较。仿真结果表明,PF算法比EKF算法滤波精度更高,RPF的跟踪性能优于PF和RPF,而且随着粒子数的增加,PF和RPF的跟踪性能也不断提高。