面对噪音时背景扣除的技巧
背景扣除是计算机视觉中的一个重要原语。我正在研究已经开发的不同方法，并且我已经开始考虑如何在面对随机、椒盐噪声时执行背景扣除。
在 Microsoft Kinect 等系统中，红外摄像头会相当一致地发出随机噪声。如果您尝试从深度视图中减去背景，如何在可靠地减去背景的同时避免这种随机噪声的问题？
Background subtraction is an important primitive in computer vision. I'm looking at different methods that have been developed, and I've begun thinking about how to perform background subtraction in the face of random, salt and pepper noise.
In a system such as the Microsoft Kinect, the infrared camera will give off random noise pretty consistently. If you are trying to background subtract from the depth view, how can you avoid an issue with this random noise while reliably subtracting the background?
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正如您已经说过的，背景的噪音和其他不稳定部分可能会导致分割问题，我的意思是背景中的灯光变化或其他移动的东西。
但如果您正在从事一些室内项目，这应该不是什么大问题，当然除了噪音问题。
除了从图像中减去背景来分割其中的对象之外，您还可以尝试将两个（或在某些方法中甚至是三个）后续帧相互减去。如果相机稳定，则应该留下已更改的部分，因此基本上是已移动的对象。所以这是一种检测移动物体的简单方法。
但在您可能使用的大多数操作中，您可能会遇到您所描述的噪音。消除它的最简单方法是在分割的二值图像上使用中值滤波器或形态运算符（开运算）。这应该可以有效地去除小部分并留下物体的漂亮大斑点。
希望有帮助...
as you already said, noise and other unsteady parts of your background might give problems in segmentation, I mean lighting changes or other moving stuff in the background.
But if you're working on some indoor-project this shouldn't be too big of an issue, except of course the noise thing.
Besides substracing the background from an image to segment the objects in it you could also try to subtract two (or in some methods even three) following frames from each other. If the camera is steady this should leave the parts that have changed, so basically the objects that have moved. So this is an easy method for detecting moving objects.
But in most operations you might use you probably will have that noise you described. Easiest way to get rid of it is by using Median Filter or Morpholocigal Operators (Opening) on the segmented binary image. This should effectively remove small parts and leave the nice big blobs of the objects.
Hope that helps...
通常，您会在视差空间中执行连接组件 (cc)，然后删除任何尺寸较小的 cc。大小和连通性的阈值（例如，两个相邻像素之间的视差差异是多少，仍然认为它们是连接的）是您要使用的两个参数（[电子邮件受保护]）。
typically you do connected components (cc) in disparity space and then kill any cc that have a small size. The threshold for size and for connectedness (e.g. what is the disparity difference between two adjacent pixel to still consider them connected) are your two parameters to play with ([email protected]).
正如@evident 提到的，中值过滤器就是你的门票。这是在保留边缘的同时消除椒盐噪声的标准运算符。
也就是说，我不同意他的建议，即这种情况发生在分割的二值图像上。中值过滤是非常低级的，应该在任何后续处理之前应用于原始数据。
As @evident mentioned, median filter is your ticket. That's the standard operator for getting rid of salt-and-pepper noise while being edge-preserving.
That said, I disagree with his suggestion that this occur on the segmented binary image. Median filtering is very low-level and should be applied on the raw data before any subsequent processing.