C 语言的 Canny 边缘检测器
我正在寻找有关 Canny 边缘检测中算法如何实现的一些说明 - 维基百科条目 - 工作。使用 2D 高斯滤波器执行降噪似乎非常简单，但我听说使用两个 1D 滤波器 - 这是如何实现的？计算梯度和边缘方向也很简单。然而，在执行非极大值抑制时，是否有一个巧妙的技巧来获得圆角？我目前正在做的是将边缘方向 (theta) 值除以 pi/4，将其转换为整数并使用 switch 语句。但是，如何处理负 theta 值 - 即 -pi/4 应该以与 3*pi/4 相同的方式处理还是与 pi/4 相同的方式处理？
非常感谢任何建议/链接！
谢谢， 本
I am looking for a bit of clarification on how the algorithms implemented in Canny edge detection - Wikipedia entry - work. It seems pretty straightforward to perform noise reduction using a 2D Gaussian filter, but I have heard that using two 1D filters - how is this accomplished? It's also simple to calculate the gradient and edge direction. However, when performing non-maximum suppression is there a neat trick to getting the rounded angle? What I'm currently doing is dividing the edge direction (theta) value by pi/4, casting it to an integer and using a switch statement. But, how does one handle the negative theta values - ie should -pi/4 be handled the same way as 3*pi/4 or the same as pi/4?
Any advice/links are much appreciated!
Thanks,
Ben
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高斯分布：
[为简单起见，省略了常数]
g2d(x,y)=exp(-xxyy)=exp(-x^2) * exp(- y^2)=g1d(x) * g1d(y)
因此可以分解为一维分布的乘法。因此，可以首先在 x 方向（每行独立）然后在 y 方向（每列独立）进行过滤
圆角：
如果角度超出 [0..pi)在这种情况下，根据需要多次加/减 pi（或使用函数 fmod）是正确的，对于 [0..pi) 一切都很清楚。
另外，根据平台的不同，最好完全避免使用反正切：您可以画一个圆，将其分为 4 个区域，并为仅使用算术运算的梯度分量生成一组条件，并给出区域方向的答案。
Gauss distribution:
[constants are omitted for simplicity]
g2d(x,y)=exp(-xx-yy)=exp(-x^2) * exp(-y^2)=g1d(x) * g1d(y)
Thus is can be separated into multiplication of 1d-distributions. And thus filtration can be done first in x-direction (independently on each row) and then in y-direction (independently on each column)
rounded angle:
If angle is outside of [0..pi) it's correct in this case to add/subtract pi as many times as needed (or use function fmod), and for [0..pi) all is clear.
Also depending on platform it may be better to avoid arctan usage at all: you can draw a circle, divide it in 4 areas and produce a set of conditions for gradient components which use only arithmetic operations and give you answer in which area direction is.
您必须自己实现还是可以使用库？ OpenCv 是一个巨大的计算机视觉 C 算法库，包括边缘检测：http ://opencv.willowgarage.com/documentation/image_processing.html?highlight=canny#cvCanny 。
如果您出于教育目的而这样做，我建议考虑购买有关计算机视觉的优秀书籍。几乎所有介绍性文本都会讨论高斯滤波（以及有据可查的一维技巧）以及精明的边缘检测和非极大值抑制。
Do you have to implement it yourself or can you use a library? OpenCv is a huge C library of algorithms in computer vision including edge detection: http://opencv.willowgarage.com/documentation/image_processing.html?highlight=canny#cvCanny .
If you are doing it for educational purposes, I suggest considering the purchase of a good text on computer vision. Almost any introductory text will discuss filtering with the Gaussian (and the well documented 1d trick) as well as canny edge detection and non-maximum suppression.
我认为 -pi/4 应该以与 3*pi/4 相同的方式处理，因为两者都定义相同的对角线。
如果您可以标准化渐变的角度，使其位于 [0, pi) 范围内，那么您可以使用如下所示的简单函数来量化角度：
I think that -pi/4 should be handled the same way as 3*pi/4, because both define the same diagonal.
If you can normalize the angle of the gradient so that it lies in [0, pi), then you could use a simple function like the following to quantize the angle: