解释Hough变换 [英] Explain Hough Transformation

问题描述

我只是冒险，并采取我的第一步走向计算机视觉。我试图自己实施霍夫变换，但我没有得到整个图像。我读过维基百科条目，甚至是理查德杜达和彼得哈特原创的使用霍夫变换来检测图片中的线条和曲线，但没有任何帮助。

有人可以用更友好的语言向我解释吗？ p>在矩形坐标中考虑一条线比较常见，即 = mx + b 。正如维基百科文章所述，一条线也可以用极坐标表示。霍夫变换充分利用了这种表示方式的变化（无论如何，这些讨论也可以应用于圆形，椭圆等）。

Hough的第一步变换就是将图像缩小到一组边缘。 Canny边缘检测器是常用的选择。最终的边缘图像作为Hough过程的输入。

总之，边缘图像中的点亮像素被转换为极坐标形式，即它们的位置被表示使用方向<θ>θ和距离 - 而不是 和 y 。 （图像的中心通常用作这种坐标变换的参考点。）

Hough变换本质上是一个直方图。映射到相同theta和r的边缘像素被假定为定义图像中的一条线。为了计算出现的频率，将θ和θ离散化（划分为多个分箱）。一旦所有的边缘像素都被转换为极坐标形式，分析箱就可以确定原始图像中的线。

寻找N 最常见的参数 - 或者将参数阈值设置为小于一些 n 的计数被忽略。

我不确定这个答案比你最初提出的来源更好 - 是否有一个特定的点你坚持？

I am just being adventurous and taking my first baby step toward computer vision. I tried to implement the Hough Transformation on my own but I just don't get the whole picture. I read the wikipedia entry, and even the original "use of the hough transformation to detect lines and curves in pictures" by richard Duda and Peter Hart, but didn't help.

Can someone help explaining to me in a more friendly language?

解决方案

It's more common to think of a line in rectangle coordinates, i.e. y = mx + b. As the Wikipedia article states, a line can also be expressed in polar form. The Hough transform exploits this change of representation (for lines, anyway. The discussion can also be applied to circles, ellipses, etc.).

The first step in the Hough transform is to reduce the image to a set of edges. The Canny edge-detector is a frequent choice. The resulting edge image serves as the input to the Hough process.

To summarize, pixels "lit" in the edge image are converted to polar form, i.e. their position is represented using a direction theta and a distance r - instead of x and y. (The center of the image is commonly used as the reference point for this change of coordinates.)

The Hough transform is essentially a histogram. Edge pixels mapping to the same theta and r are assumed to define a line in the image. To compute the frequency of occurrence, theta and r are discretized (partitioned into a number of bins). Once all edge pixels have been converted to polar form, the bins are analyzed to determine the lines in the original image.

It is common to look for the N most frequent parameters - or threshold the parameters such that counts smaller than some n are ignored.

I'm not sure this answer is any better than the sources you originally presented - is there a particular point that you are stuck on?

这篇关于解释Hough变换的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！