Image processing [closed] - c++

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I have several bitmaps images and want to segment and access the position (and want know the size) of the continuous tone very high correlated areas,I mean by continues tone areas (high correlated segments) only the the areas that hold the exact same values of pixels. I have experience in image processing and am using c++ and opencv but i didnt find a library doing that am afraid if I do the programming I will lose the performance and the calculation became inefficient while I need to process further a lot of things. in this time but due to 10 years of this science leaving I became clumsy and I cant find the answers as I were young, I will be grateful if you help me in any ideas about that because am stuck. thanks for the kind reading and help.

Answer 1

I can demonstrate the concept of my comment using ImageMagick, and this test image which has the useful property of being noise which means you can see it on SO's white background and the algorithm shouldn't see it.
I can average it over an area of 15x15 like this:
convert test.png -statistic mean 15x15 x.png
which gives this
then threshold and invert it so you can see the areas of continuous tone identified in white
convert test.png -statistic mean 15x15 test.png -compose difference -composite -depth 8 -threshold 1 -negate x.png
You can experiment with different widths and heights of the blurring box like this:
#!/bin/bash
for x in 3 7 15 25; do
for y in 3 7 15 25; do
convert -label "${x}x${y}" test.png -statistic mean ${x}x${y} miff:-
done
done | montage - -frame 5 -tile 4x out.png
which gives this:
and the corresponding masked image thus:
You can the pass that into the Connected Components Analysis like this:
convert test.png -statistic mean 5x5 \
test.png -compose difference -composite \
-depth 8 -threshold 1 -negate \
-define connected-components:verbose=true \
-define connected-components:area-threshold=20 \
-connected-components 8 -auto-level blobs.png
which will give you this which contains the coordinates of the blobs
Objects (id: bounding-box centroid area mean-color):
0: 500x500+0+0 270.8,271.7 177169 srgb(0,0,0)
1: 216x216+52+41 159.5,148.5 46656 srgb(255,255,255)
8: 114x114+63+351 119.5,407.5 10039 srgb(255,255,255)
2: 81x100+354+47 394.0,96.5 8100 srgb(255,255,255)
5: 49x49+348+204 372.0,228.0 2401 srgb(255,255,255)
6: 358x5+55+287 233.5,289.0 1790 srgb(255,255,255)
10: 45x45+244+383 265.9,405.0 1520 srgb(255,255,255)
3: 4x289+451+181 452.5,325.0 1156 srgb(255,255,255)
7: 122x4+57+309 117.5,310.5 488 srgb(255,255,255)
9: 4x114+416+356 417.5,412.5 456 srgb(255,255,255)
4: 15x15+312+185 319.0,192.0 225 srgb(255,255,255)
I can then outline the detected areas on top of the original image: