我的函数的输出不同于conv2（）输出 [英] My function's output differs from conv2() output

问题描述

下面是我编写的用x方向Sobel掩码对图像进行卷积的代码。该函数输入灰度图像以及要卷积的核。但是，我得到的结果与从内置conv2（）函数获得的结果不同。这是为什么？

Below is the code I have written to convolve an image with a x-direction Sobel mask. The function inputs a gray-scale image along with a kernel to be convolved with. However, the result I am getting is different compared to the result I get from the built-in conv2() function. Why is that? What am I doing wrong?

这是我的函数输出：

这里是conv2（）输出：

Here's conv2() output:

推荐答案

您正在计算相关性。 2D卷积是内核的180度旋转（翻转两个轴）。

You are computing the correlation. The 2D convolution is a 180 degree rotation (flip both axes) of the kernel.

h = rot90(h,2);

你不会注意到对称滤波器的区别，比如高斯或平均内核，衍生品将在两个方向上反转。

You won't notice the difference with a symmetric filter, like a Gaussian or an averaging kernel, but a derivative will be reversed in both directions.

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