During contour evolution, the particle filter (PF) is used to track the feature points by enforcing spatiotemporal local constraints to handle the observation noise.
为避免观测噪声的影响，增加轮廓的时空局部约束并利用粒子滤波(PF)技术解决该类跟踪问题是非常有效的。
Particle filter (PF) is an optimal nonlinearity filtering method which rises in recent years.
粒子滤波（PF）是近年来兴起的一种最优非线性滤波方法。
Compared with the particle filter (PF), it avoids the resampling step and the particle degeneracy phenomenon.
与粒子滤波算法相比，其优点是不需要重采样步骤和不存在粒子退化现象。
Aiming at multisensor fusion based target tracking applications in wireless sensor networks, a mixed algorithm is proposed, called extended-mixed particle filter (EM-PF).
针对无线传感器网络中的多传感器融合目标跟踪，提出一种混合滤波算法，称为扩展混合粒子滤波算法(EM - PF)。
Since the real life systems basically are nonlinear, so this paper study the basic principles and specific applications of Particle Filter (PF) specially used for non-linear non-Gaussian tracking.
由于我们实际生活中的系统基本上都是非线性的，因此本文研究的是专门用于非线性非高斯系统跟踪的粒子滤波算法(PF)的基本原理及其具体应用。
This dissertation makes deeply research on the Mean Shift, Particle Filter (PF), Unscented Particle Filter and intelligence optimization algorithm. And some beneficial results are obtained.
本论文在均值偏移、粒子滤波、无迹粒子滤波及智能优化算法等方面进行了较为深入的研究，取得了一些有益的成果。
The research topics include approaches of robot's pose tracking, Markov localization, Particle Filter and other improved PF method.
其主要内容包括移动机器人的位姿跟踪方法、马尔可夫定位方法、粒子滤波方法及各种粒子滤波改进方法。
The research topics include approaches of robot's pose tracking, Markov localization, Particle Filter and other improved PF method.
其主要内容包括移动机器人的位姿跟踪方法、马尔可夫定位方法、粒子滤波方法及各种粒子滤波改进方法。
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