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particle filters
相关语句
  粒子滤波器
    In the first step, several independent CONDENSATION-style particle filters are utilized to track each facial feature in temporal domain.
    首先在时间域上,使用几个相互独立的Condensation类型的粒子滤波器分别跟踪人脸的每个特征。
短句来源
  粒子滤波
    Super-Resolution Reconstruction for Face Images Based on Particle Filters Method
    基于粒子滤波的人脸图像超分辨率重建方法
短句来源
    Visual tracking using particle filters based on data fusion
    一类基于信息融合的粒子滤波跟踪算法
短句来源
    Particle filters are very effective for visual tracking problems; however multiple independent trackers ignore the spatial constraints and the natural relationships among facial features.
    粒子滤波对独立的视觉跟踪问题非常有效,但是多个独立的跟踪器忽视了人脸的空间约束和人脸特征间的自然相互联系。
短句来源
    Experiments on synthetic and real image sequences of human motion demonstrate the tracking accuracy and computation efficiency of the proposed human tracking method compared with the tracking method based on particle filters.
    模拟场景和真实场景实验表明,与基于粒子滤波的人体跟踪算法相比,基于概率进化算法的人体跟踪具有较高的跟踪精度和较快的运算速度。
短句来源
    Particle filters offer a probabilistic framework for dynamic state estimation and have proven to work well in cases of clutter and occlusion.
    然后 ,采用粒子滤波的方法将颜色分布模型集成到一个动态状态估计的概率框架中 .
短句来源
  相似匹配句对
    Visual tracking using particle filters based on data fusion
    一类基于信息融合的粒子滤波跟踪算法
短句来源
    Super-Resolution Reconstruction for Face Images Based on Particle Filters Method
    基于粒子滤波的人脸图像超分辨率重建方法
短句来源
    Particle Swarm Optimization
    粒子群优化算法
短句来源
    Particle Uniformity Level Expression
    颗粒群粒度均匀性的定量表征
短句来源
    Application of the Filters in Image Processing
    滤波器在图像处理中的应用
短句来源
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  particle filters
The aim of this paper is to propose a unified tracking framework using particle filters to efficiently switch between visual tracking (field of view tracking) and track prediction (non-overlapping region tracking).
      
However, particle filters are not used in practice for these applications mainly because they cannot satisfy real-time requirements.
      
There are many applications in which particle filters outperform traditional signal processing algorithms.
      
Design and Implementation of Flexible Resampling Mechanism for High-Speed Parallel Particle Filters
      
The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences.
      
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Robust real-time tracking of non-rigid objects is a challenging task. Color distributions provide an efficient feature for this kind of tracking problems as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. This article presents the integration of color distributions into particle filtering, which has typically been used in combination with edge-based image features. Particle filters offer a probabilistic framework for dynamic state estimation and...

Robust real-time tracking of non-rigid objects is a challenging task. Color distributions provide an efficient feature for this kind of tracking problems as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. This article presents the integration of color distributions into particle filtering, which has typically been used in combination with edge-based image features. Particle filters offer a probabilistic framework for dynamic state estimation and have proven to work well in cases of clutter and occlusion. To overcome the problem of appearance changes, an adaptive model update is introduced during temporally stable image observations. Furthermore, an initialization strategy is discussed since tracked objects may disappear and reappear.

提出了一个利用颜色特征实时跟踪非刚性物体的方法 .首先 ,建立了一个颜色分布模型 ,该模型对部分遮挡具鲁棒性 ,对放缩和旋转具不变性 ,且计算简单 .对非刚体物体的实时鲁棒跟踪是一个非常有挑战性的课题 ,本文提出了利用颜色特征实时跟踪非刚体物体的方法 .首先 ,建立了一个颜色分布模型 ,该模型对部分遮挡具有鲁棒性 ,对放缩具有不变性 ,而且计算简单 .然后 ,采用粒子滤波的方法将颜色分布模型集成到一个动态状态估计的概率框架中 .为了处理光照变化等引起的外貌变化 ,进一步引入自适应模型更新过程 .同时 ,本文还讨论了初始化策略用以处理跟踪物体的消失或消失后再出现的情况

In the research of medical image processing, motion estimation and tracking relating to the region of interest has been given considerable attention. For improving the quality of the noisy or cluttered medical images, the particle filter (PF) based on the non-linear and non-Gaussian Bayesian State Estimation is a better as well as a technically challenging solution. As the algorithm of particle weights, especially the importance density function, often severely affects the performance of the PF,...

In the research of medical image processing, motion estimation and tracking relating to the region of interest has been given considerable attention. For improving the quality of the noisy or cluttered medical images, the particle filter (PF) based on the non-linear and non-Gaussian Bayesian State Estimation is a better as well as a technically challenging solution. As the algorithm of particle weights, especially the importance density function, often severely affects the performance of the PF, we propose in this paper a better algorithm for its improvement; in addition, to ensure better tracking of the dynamic contour with the PF, we proposed a new algorithm for the likelihood and prior probability density. Objective theoretical evaluation and substantial comparative experiments suggest that this method can be a good solution for accurate dynamic contour tracking.

关于医学图像的研究,感兴趣区的运动估计和跟踪是一个深受关注的领域。鉴于医学图像质量低、噪声大的普遍特点,从状态变量的非线性、非高斯分布前提出发,利用粒子滤波技术解决该类跟踪问题是一种具有挑战性的技术:由于经典粒子滤波器的权值计算,尤其是重要密度函数的构造方法严重影响了粒子滤波器的性能,本文提出了重要改进。针对用粒子滤波方法估计动态轮廓线这一特殊应用,构造了具有特色的似然和先验概率密度算法。结合客观的理论评价标准和大量比较试验,该方法为精确估计动态轮廓线提供了较好的解决对策。

Tracking multiple facial features simultaneously is a challenge when rich expressions are presented on a face Several independent condensation-style particle filters are utilized to track each facial feature in temporal domain Particle filters are very effective for visual tracking problems, however multiple independent trackers ignore the natural relationships among facial features We use Bayesian inference-belief propagation to infer each facial feature's contour in spatial domain, taking into...

Tracking multiple facial features simultaneously is a challenge when rich expressions are presented on a face Several independent condensation-style particle filters are utilized to track each facial feature in temporal domain Particle filters are very effective for visual tracking problems, however multiple independent trackers ignore the natural relationships among facial features We use Bayesian inference-belief propagation to infer each facial feature's contour in spatial domain, taking into consideration the previously extracted relationships among contours of facial features which are organized as a large facial expression database Experimental results show that our algorithm is robust

同时跟踪具有丰富表情的人脸多个特征是一个有挑战性的问题 提出了一个基于时空概率图模型的方法 在时间域上 ,使用几个相互独立的Condensation类型的粒子滤波器分别跟踪人脸的每个特征 粒子滤波对独立的视觉跟踪问题非常有效 ,但是多个独立的跟踪器忽视了人脸的空间约束和人脸特征间的自然相互联系 ;在空间域上 ,事先从人脸表情库中学习人脸特征轮廓的相互关系 ,使用贝叶斯推理 -信任度传播算法来对人脸特征的轮廓位置进行求精 实验结果表明 ,文中算法可以在帧间运动较大的情况下 ,鲁棒地同时跟踪人脸多个特征

 
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