在圈内Hough变换，什么是累加器分辨率（DP），它是如何影响圆检测的反比？ [英] In the Circle Hough Transform, what is the Inverse Ratio of Accumulator Resolution (dp) and how does it affect circle detection?

问题描述

在OpenCV文档状态：

The OpenCV documentation states:

DP：累加器分辨率图像分辨率的反比。例如，如果DP = 1，累加器具有相同的分辨率的输入图像。如果DP = 2，蓄能器具有的一半大的宽度和高度。

dp: Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height.

但它不会给出指示该值的大小如何影响圆检测。我以为蓄能器只是一个极大的集合，它是如何有一个决议？

But it gives no indication how the size of this value affects circle detection. I thought the accumulator was just a collection of maxima, how does it have a resolution?

推荐答案

在Hough变换你改变你的输入图像进行所谓的霍夫空间。它是在试图找到界3维（三个维度是圆心和半径的坐标）。在转换过程中的输入图像的每个选票边缘像素为所有可能的圆圈上的像素可能说谎。

During hough transformation you transform your input image into so called hough space. It is 3-dimensional while trying to find circles (the three dimensions are the coordinates of the circle center and the radius). During the transformation each edge pixel in your input image votes for all possible circles on which the pixel could lie.

您可以把投票作为一个3维矩阵（霍夫空间）内增加多个值。表决后，你搜索这个基体中的最高值，圆心和半径的阅读。

You can think of the voting as increasing multiple values within a 3-dimensional matrix (hough space). After voting you search for the highest value within this matrix and read of the circle center and its radius.

更大的矩阵（与你的输入图像）（越小你的 DP ），您的投票分辨率就越高。分辨率越高，越精确圆检测。

The bigger the matrix (compared to your input image) (the smaller your dp), the higher the resolution of your voting. The higher the resolution, the more accurate the circle detection.

然而，更精确的检测，越有可能是到例如思念退化略圆或检测多个圆圈，而不是一个具有很大的优势。

However, the more accurate the detection, the more likely it is to e.g. miss slightly degenerated circles or detect multiple circles instead of one with a big edge.

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