In the color image sequence, the mean shift algorithm is an efficient method for tracking object.
在彩色序列图像目标跟踪中，均值移位算法是一种有效的方法。
The mean shift algorithm is a nonparametric statistical method for seeking the nearest mode of a point sample distribution.
均值移位算法是一种搜索与样本点分布最相近模式的非参数统计方法。
Secondly, we use the mean shift algorithm to track the faces. We realize automatic face tracking of image sequence eventually.
其次，在人脸跟踪方面采用基于均值平移的算法进行跟踪，最终实现图像序列的自动人脸跟踪。
Therefore, the kernel-tracking algorithm in our system is based on the mode of histogram and the search method of Mean Shift algorithm.
因此，本系统使用以直方图为模式特征，以均值平移算法为计算方法的实时跟踪算法为本系统的核心跟踪算法。
First, fast mean shift algorithm is used to over-segment the image. Second, improved LI-method is used to automatically segment the image.
文中首先利用快速均值漂移算法对图像进行过度分割，然后再利用改进的水平集方法进行分割， 最后由轮廓提取算法将感兴趣区域提取出来。
Through the combination of adaptive gray scale bandwidth and threshold-based region mergence, we proposed a novel modified scheme of mean shift algorithm.
本文通过灰度域带宽的自适应选取和阈值法区域合并的结合，提出对均值漂移图像分割算法的改进方案。
To improve the efficiency of graph cut algorithm and reduce user interaction, the graph cut and mean shift algorithm were combined to automatically segment 3D vertebra images.
为提高图割算法的效率并减少用户交互量，提出将图割与均值漂移算法结合应用的脊椎骨自动分割方法。
Mean-Shift: Algorithm that does not require any a priori knowledge about the number of clusters and can produce arbitrarily shaped clusters.
Mean - Shift：无需任何关于集群数量的推理知识的算法，它可以生成任意形状的集群。
Mean shift is an effective iterative algorithm widely used in clustering, tracking, segmentation, discontinuity preserving smoothing, filtering, edge detection, and information fusion etc.
均值平移是一种有效的统计迭代算法，已广泛应用于聚类分析、跟踪、图像分割、图像平滑、滤波、图像边缘提取和信息融合等方面。
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。
Finally we use this dynamic model of optimization in mean-shift algorithm, and compute object's true position.
最后将最优化的运动模型用于基于核的均值转移算法中，从而获得运动目标的精确位置。
The Gaussian kernel mean-shift algorithm which is deduced from kernel density estimation has not been widely employed in applications because of its low convergence rate.
由核密度估计推导获得的高斯核均值漂移算法因收敛速度慢在应用中效率不高。
The results demonstrated that the algorithm has great improvement relative to mean shift, it could detect and track the video moving target continuously and had preferable robustness for occlusion.
实验结果表明，本算法相对于均值偏移算法有较好的改进，能对视频中的运动目标实现检测和连续跟踪，对遮挡也有较好的鲁棒性。
Mean shift based image segmentation algorithm is a kind of kernel density estimation based feature space analysis algorithm, and the nature of it is statistical optimization.
均值漂移算法是一种基于核密度梯度估计的特征空间分析算法，其实质是一种统计优化过程。
The hybrid algorithm first filters an image using mean shift technique and then segments the image with graph based segmentation algorithm.
混合算法首先利用均值漂移技术对图像进行滤波，然后再使用图分割算法对图像进行分割。
Experiments' results show that the mean shift based algorithm is more sensitive than the graph based algorithm and the hybrid algorithm, which are very stable to parameters and different images.
实验结果显示，均值漂移算法分割结果对其参数变化较为敏感，而基于图模型的算法和混合算法则较为稳定。
In the part of tracking, combining the improved Mean-Shift algorithm and Kalman filter prediction.
在跟踪部分，采用改进的均值漂移算法和卡尔曼滤波预测结合。
For reducing noise and preferably keeping the edge information, the mean-shift algorithm based on the HSV space was proposed.
为了能够更好的去除图像的噪声和很好的保留图像的边缘信息，提出了基于HSV空间的均值移动平滑算法。
In view of characteristics for coke optical texture in micrograph, a segmentation algorithm, combining mean shift and edge confidence, is proposed.
针对焦炭显微图像中光学组织的特点，提出了一种结合均值偏移和边缘置信度的焦炭显微图像分割。
In view of characteristics for coke optical texture in micrograph, a segmentation algorithm, combining mean shift and edge confidence, is proposed.
针对焦炭显微图像中光学组织的特点，提出了一种结合均值偏移和边缘置信度的焦炭显微图像分割。
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