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    • 44. 发明申请
    • System and process for tracking an object state using a particle filter sensor fusion technique
    • 使用粒子滤波器传感器融合技术跟踪物体状态的系统和过程
    • US20050114079A1
    • 2005-05-26
    • US10985243
    • 2004-11-10
    • Yong RuiYunqiang Chen
    • Yong RuiYunqiang Chen
    • G06T7/20G10L21/02G01S13/00
    • G06T7/277G10L2021/02166
    • A system and process for tracking an object state over time using particle filter sensor fusion and a plurality of logical sensor modules is presented. This new fusion framework combines both the bottom-up and top-down approaches to sensor fusion to probabilistically fuse multiple sensing modalities. At the lower level, individual vision and audio trackers can be designed to generate effective proposals for the fuser. At the higher level, the fuser performs reliable tracking by verifying hypotheses over multiple likelihood models from multiple cues. Different from the traditional fusion algorithms, the present framework is a closed-loop system where the fuser and trackers coordinate their tracking information. Furthermore, to handle non-stationary situations, the present framework evaluates the performance of the individual trackers and dynamically updates their object states. A real-time speaker tracking system based on the proposed framework is feasible by fusing object contour, color and sound source location.
    • 提出了一种使用粒子滤波器传感器融合和多个逻辑传感器模块跟踪物体状态随时间变化的系统和过程。 这种新的融合框架将自下而上和自顶向下的方法与传感器融合相结合,以概率地融合多种感测模式。 在较低级别,个人视觉和音频跟踪器可以设计用于为定影器生成有效的建议。 在较高级别,定影器通过从多个线索的多个似然模型上验证假设来执行可靠的跟踪。 与传统融合算法不同,本框架是闭环系统,其中定影器和跟踪器协调其跟踪信息。 此外,为了处理非平稳情况,本框架评估各个跟踪器的性能并动态更新其对象状态。 基于提出的框架的实时扬声器跟踪系统可以通过融合对象轮廓,颜色和声源位置来实现。
    • 49. 发明授权
    • System and method for adaptive spatial compounding for ultrasound imaging
    • 用于超声成像的自适应空间复合的系统和方法
    • US07817839B2
    • 2010-10-19
    • US11566328
    • 2006-12-04
    • Yunqiang ChenJason Jenn-Kwei Tyan
    • Yunqiang ChenJason Jenn-Kwei Tyan
    • G06K9/00
    • A61B8/00A61B8/5238
    • A method for removing speckle noise from ultrasound images includes providing a plurality of digitized ultrasound (US) images, each image comprising a plurality of intensities corresponding to a domain of points on a 2-dimensional grid, initializing an initial gain associated with each of said plurality of US images, estimating a signal sub-space by averaging over each US image divided by its associated gain, and estimating an updated gain by projecting its associated image into said signal sub-space. If an absolute difference of said updated gain and said initial gain is less than a pre-determined quantity, obtaining an averaged image from said signal sub-space, estimating an optimal Wiener filter from said plurality of US images and said averaged image, and filtering said averaged image with said Wiener filter, wherein said speckle noise is substantially minimized.
    • 一种用于从超声图像中去除斑点噪声的方法包括提供多个数字化超声波(US)图像,每个图像包括对应于二维网格上的点的域的多个强度,初始化与每个所述 多个美国图像,通过对每个US图像进行平均而除以其相关联的增益来估计信号子空间,以及通过将其相关图像投影到所述信号子空间来估计更新的增益。 如果所述更新的增益和所述初始增益的绝对差小于预定量,则从所述信号子空间获得平均图像,从所述多个US图像和所述平均图像中估计最佳维纳滤波器,以及滤波 所述维纳滤波器的所述平均图像,其中所述斑点噪声基本上最小化。
    • 50. 发明授权
    • Mode-based multi-hypothesis tracking using parametric contours
    • 基于模式的多假设跟踪使用参数轮廓
    • US07231064B2
    • 2007-06-12
    • US11282365
    • 2005-11-17
    • Yong RuiYunqiang Chen
    • Yong RuiYunqiang Chen
    • G06K9/00
    • G06K9/00234G06K9/3216G06K9/6207G06T7/251G06T7/277G06T2207/10016G06T2207/30201
    • A system and method for object tracking using probabilistic mode-based multi-hypothesis tracking (MHT) provides for robust and computationally efficient tracking of moving objects such as heads and faces in complex environments. A mode-based multi-hypothesis tracker uses modes that are local maximums which are refined from initial samples in a parametric state space. Because the modes are highly representative, the mode-based multi-hypothesis tracker effectively models non-linear probabilistic distributions using a small number of hypotheses. Real-time tracking performance is achieved by using a parametric causal contour model to refine initial contours to nearby modes. In addition, one common drawback of conventional MHT schemes, i.e., producing only maximum likelihood estimates instead of a desired posterior probability distribution, is addressed by introducing an importance sampling framework into MHT, and estimating the posterior probability distribution from the importance function.
    • 使用基于概率模式的多假设跟踪(MHT)的对象跟踪的系统和方法提供了在复杂环境中运动对象(例如头部和面部)的鲁棒和计算上有效的跟踪。 基于模式的多假设跟踪器使用在参数状态空间中从初始样本精化的局部最大值的模式。 由于模式具有很高的代表性,所以基于模式的多假设跟踪器使用少量假设来有效地建模非线性概率分布。 通过使用参数因果轮廓模型来将初始轮廓细化到附近模式,可以实现实时跟踪性能。 另外,常规MHT方案的一个共同缺点,即仅产生最大似然估计而不是期望的后验概率分布,通过将重要性采样框架引入到MHT中,并从重要性函数估计后验概率分布来解决。