Over the past three years, parallel clustering has begun to change the face of supercomputing.
在过去的三年里，并行集群已在改变着超级计算的面貌。
In this paper a method of face recognition based on clustering algorithm and hierarchical support vector machines is presented.
提出一种基于聚类算法和层次支持向量机的人脸识别方法。
This algorithm can be used in data clustering and face detection. Its effectiveness has been proven by the experiment results.
这个算法可以用于数据聚类和人脸识别方面，实验结果也证明了该算法的效果。
A method for human face detection under complex background is proposed on the basis of human vision mechanism and skin color clustering features.
在人类视觉机制和肤色聚类特性的基础上，提出了一种复杂背景下人脸检测方法。
We explore the clustering characteristics on xanthous face, and point out that HSV color model can be well used to express the clustering characteristics of xanthous faces.
通过对S色彩分量空间的阈值分割，利用分割后的图像特点与标准脸的投影进行比对，实现对检测到的人脸进行认证。
We explore the clustering characteristics on xanthous face, and point out that HSV color model can be well used to express the clustering characteristics of xanthous faces.
通过对S色彩分量空间的阈值分割，利用分割后的图像特点与标准脸的投影进行比对，实现对检测到的人脸进行认证。
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