The enabling technolo-gies borrow from the fields of modeling, data processing, Bayesian estimation theory and in particular a technique called particle filtering. The efficiency of the diagnostic approach is demonstrated via simulation results. 展开 ...
The positive value ofD_{\mathrm{KLD}} (p{||}q)represents the information gain achieved frompinstead of the random modelq. Based on Bayesian inference,D_{\mathrm{KLD}} (p\parallel q)is the information gained upon measurement having posterior probability distributionpcompared to the priori known...
In most multi-frame image super-resolution (SR) studies, the process that models the construction of the low-resolution (LR) images from high resolution (HR) one includes geometric transformation, blurring, downsampling, and adding noise. A simple linear approximation of this model is used in ...
Bayesian methods provide a rigorous general framework for dynamic state estimation problems. We describe the nonlinear/non-Gaussian tracking problem and it... S Maskell,N Gordon - Target Tracking: Algorithms & Applications 被引量: 1.6万发表: 2001年 Taylor & Francis Online :: Sequential Monte Carlo...
Given the state of the object xt at time t and the frame based observations z1:t up to time t, the posterior probability p(xt|z1:t) can be updated by the Bayesian filter State transition model As the goal of our tracking method is to find the best state of the object using given...
The estimated parameters of the radioactive source obtained from the t-th observation vector through the Bayesian model are expressed as Zt=[xt,yt,zt]T. Z1:t−1={Z1,Z2,⋅⋅⋅,Zn} refers to the set of observation vectors from the initial time to time t-1. The posterior ...
Predictive estimation of radioactive sources using Bayesian theory is of great importance in nuclear emergency response. To this aim, we put forward an unscented particle filtering algorithm based on divide-and-conquer sampling. Our method exploits the information acquired by mobile detection robots to...
Particle filtering, a sequential importance sampling method, is a Bayesian state estimation method based on Monte Carlo method and the core idea is to construct a posteriori probability of the system state using known information. Since the particle filter does not require the system to be linear ...
Particle Filters are Bayesian state estimators based on Monte Carlo simulation. These filters numerically solve the a posteriori PDF by random samples, called particles. In particular, the propagation of uncertainties is a characteristic of Monte Carlo sampling methods [43]. While Particle Filters for...
bayesiandecision ; targetocclusion ! 引言 目标跟踪是计算机视觉领域中最活跃的研究主题之一,其 核心是利用计算机视觉技术从图像序列中检测、跟踪、识别目 标并对其行为进行理解与描述,它在军事视觉制导、机器人视 觉导航、安全检测、交通流量监测、医疗诊断以及气象分析等方 面都有广泛的应用 . 目标跟踪的过程是一个...