如何计算图像末端的lbp码? [英] How to calculate the lbp codes at the ends of the images?

问题描述

例如，坐标为(1，1)的像素的lbp代码可以使用像素(0，0)进行计算； (0，1); (0，2); (1，2); (2，2); (2，1); (2，0); (1，0)，但极值像素不具有这8个邻域像素，即，像素(0，0)仅具有3个邻域.

For example, the lbp code of the pixel with coordinate (1, 1) is possible to calculate it with the pixels (0, 0); (0, 1); (0, 2); (1, 2); (2, 2); (2, 1); (2, 0); (1, 0) but the pixels of the extremes do not have those 8 neighborhood pixels, that is, the pixel (0, 0) only has 3 neighbors.

出现这个问题是因为我已经使用sicikit图像获得了LBP图像，代码如下:

This question comes to me because I have obtained the LBP image using sicikit image, the code is as follows:

lbp = feature.local_binary_pattern (gray, 8, 1, 'ror')

然后我打印了灰色图像的值并得到了这些值:

Then I printed the values ​​of the gray image and got these values:

[[185 185 190 ... 176 172 178]]
 [183 180 181 ... 194 185 175]
 [203 199 199 ... 201 193 179]
 ...
 [205 188 182 ... 183 183 182]
 [207 197 194 ... 193 190 186]
 [206 201 201 ... 201 199 197]]

我还打印了LBP图像的值并得到了这些值:

I also printed the values ​​of the LBP image and got these values:

[[  1.  17.   1. ...  15.  31.   1.]
 [ 27. 255. 127. ...   7.   7.  31.]
 [  0.  31.  31. ...   1.  31.  15.]
 ...
 [ 17.  31.  63. ...  63. 111.  31.]
 [  0.  31.  31. ...  15.  15.   7.]
 [  1.  25.  17. ...   0.   1.   1.]]

我知道，例如，右上角像素的lbp码是正确的，因为它提供的值为7，但是我不明白如何获得极限值的LBP码.谢谢.

I understand that, for example, the lbp code of the pixels on the top right is correct since it provides a value of 7 but I do not understand how the LBP codes of the extremes are obtained. Thanks.

推荐答案

函数

The function skimage.feature.local_binary_pattern performs zero padding under the hood. As a consequence of it the LBP codes are actually computed from the padded image:

[[  0   0   0   0 ...   0   0   0   0]
 [  0 185 185 190 ... 176 172 178   0]
 [  0 183 180 181 ... 194 185 175   0]
 [  0 203 199 199 ... 201 193 179   0]
 ...
 [  0 205 188 182 ... 183 183 182   0]
 [  0 207 197 194 ... 193 190 186   0]
 [  0 206 201 201 ... 201 199 197   0]
 [  0   0   0   0 ...   0   0   0   0]]

在上图使用'ror'方法时，与最左上角像素相对应的LBP为:

When you use the 'ror' method on the image above, the LBP corresponding to the top left most pixel is:

 0   0   0           0 0 0
 0  185 185    >>    0   1    >>    00000001    >>    1
 0  183 180          0 0 0  

第一行第二个像素对应的LBP变为:

The LBP corresponding to the second pixel on the first row turns out to be:

 0   0   0           0 0 0
185 185 190    >>    1   1    >>    00010001    >>    17
183 180 181          0 0 0  

与最右上角像素相对应的LBP为:

The LBP corresponding to the top right most pixel is:

 0    0   0          0 0 0
172  178  0    >>    0   0    >>    000000001    >>    1
185  175  0          1 0 0  

...等等.

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