k表示使用现有信息进行分割
所以我遇到了这个问题，它说：
您需要在绿色（草）背景下识别移动的（红色）板球。假设彩色图像和光流场都可用，请简要描述如何使用 K 均值分割来解决此任务。
我的答案是 - 定义 2 个簇，因为有两种颜色。由于您拥有光流，因此您可以识别板球，并使用该球内的聚类来得出每个聚类的平均值。然后开始k-means。
你觉得我的解释有道理吗？如果是这样，我不明白为什么我们需要使用 k 均值算法来分割我们已经知道其光流的图像（因此可以开始分割）。想法？感谢您的帮助！
so i came across this question which says:
You need to identify a moving (red) cricket ball against a green (grass) background. Assuming that both a colour image and an optic flow field are available, briefly describe how you would address this task using K-means segmentation.
my answer would be- define 2 clusters as there are two colours. since you have the optic flow, you can identify the cricket ball, and use clusters within that ball to come up with the mean of each cluster. then start k-means.
do you think my explanation makes sense? if so, i dont get why we need to use k-means algorithm to segment an image whose optic flow we already know (hence can segment to begin with). thoughts? thanks for any help!
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您可以使用 k 均值根据流量查找所有对象（草和球）的平均速度。因此场景可以分为移动物体（球）和非移动物体（草）..这是我的猜测...
you can use k-means to find the average velocity of all the objects (both grass and ball) on the basis of the flows. hence the scene can be divided into moving objects (ball) and non-moving objects (grass).. that would be my guess...