opencv通过分段检测楼层 [英] opencv floor detection by segmentation

问题描述

我正在寻找一种检测图像中底部的方法。我试图通过将图像减少到颜色区域，然后假设最大的区域是地板来实现这一点。 （我们得到做出关于环境的机器人将在一些操作相当广泛的假设）

I'm working on a a way to detect the floor in an image. I'm trying to accomplish this by reducing the image to areas of color, and then assuming that the largest area is the floor. (We get to make some pretty extensive assumptions about the environment the robot will operate in)

我正在寻找的是在算法上的一些建议，将适用于本问题。任何帮助将非常感激。

What I'm looking for is some recommendations on algorithms that would be suited to this problem. Any help would be greatly appreciated.

编辑：具体我正在寻找一个图像分割算法，可以可靠地提取一个区域。我试过的所有（主要是PyrSegmentation）似乎工作通过减少图像到N颜色。当相机正在查看空白区域时，这会导致误报。

specifically I am looking for an image segmentation algorithm that can reliably extract one area. Everything I've tried (mainly PyrSegmentation) seems to work by reducing the image to N colors. This is causing false positives when the camera is looking at an empty area.

推荐答案

由于地板检测的主要目的，我会说，而不是按颜色分割，你可以尝试通过分离<强> texture 。

Since floor detection is the main aim, I'd say instead of segmenting by color, you could try separation by texture.

的征变换阐述质感单值描述粗糙使用特征值的平均值在图像/视频帧灰度窗口。第10页。本文的78，他们运用均值漂移分割上的征变换输出的形象，有效地将其分离成不同的纹理。

The Eigen transform paper describes a single-value descriptor of texture "roughness" using the average of eigenvalues over a grayscale window in the image/video frame. On pg. 78 of the paper they apply the mean-shift segmentation on the eigen-transform output image, effectively separating it into different textures.

由于您的图像来自视频馈送，在照明中可以有很多变化，所以颜色分割可能引起一些问题（除非你使用HSV和其他颜色空间，如上所述）。在OpenCV中使用 cvSVD（）函数计算特征值非常简单和快速。

Since your images are from a video feed, there can be a lot of variations in lighting so color segmentation might pose a few problems (unless you're working with HSV and other color spaces as mentioned above). The calculation of the eigenvalues is very simple and fast in OpenCV with the cvSVD() function.

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