Image kernels
Image kernels are fun as filters, so let us just look at a few of them and maybe try something else?
Where do I get the numbers from? Awesome blog
So we start with this image
Filters
Blur
kernel_blur = [
0.0625 0.125 0.0625;
0.125 0.25 0.125;
0.0625 0.125 0.0625
]
Bottom Sobel
kernel_blur = [
-1 -2 -1 ;
0 0 0 ;
1 2 1
]
Emboss
kernel_blur = [
-2 -1 0 ;
-1 1 1 ;
0 1 2
]
Identify
kernel_blur = [
0 0 0 ;
0 1 0;
0 0 0
]
Left Sobel
kernel_blur = [
-1 0 -1 ;
-2 0 -2;
1 0 -1
]
Outline
kernel_blur = [
-1 -1 -1 ;
-1 8 -1 ;
-1 -1 -1
]
Right sobel
kernel_blur = [
-1 0 1 ;
-2 0 2 ;
-1 0 1
]
Sharpen
kernel_blur = [
0 -1 0 ;
-1 5 -1 ;
0 -1 0
]
Top sobel
kernel_blur = [
1 2 1 ;
0 0 0 ;
-1 -2 -1
]
Experiments!!
What happens when you convolve two images of the same size??
tmp_cm = channelview(Gray.(testimage("house")));
tmp_cm2 = channelview(Gray.(testimage("mandrill")));
imshow(conv2d(tmp_cm2,tmp_cm))
I get a fully white image... Is it because the images are of the same size? Since these convolutions are only in black and white.. I cheated a bit for the purpose of this experiment and used a library. (Obviously I will do it from scratch later or atleast try to).
using DSP
imshow(DSP.conv(channelview(tmp_cm),tmp_cm2))
I want to analyze a bit more. Here are the two images
So I get this.
I am not sure why? I can't visualize it atleast.
Different size?
So the largest realistic kernel size I have seen is 15. So let us take this house and resize it to that and try and see what happens.
Man I have to take a second to actually appreciate the fact that we can understand this as a house. So this is what we get.
Wow! That actually did something. You know, I am actually enjoying this detour. I should add more experiment sections whenever I can.