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I am trying to extract finger print from this image by using green LED backlight but when it convert to binarize image most of finger print is gone.

gray= green

I use this fomular to convert from this color image

Color Image

to this gray image

Gray Image

I use otsu for binarization. So Which preprocessing I should use before convert to binarization to emphasis the finger print.

Binarized Image

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  • $\begingroup$ you could just upload to imgur.com and link to that image from here, we'll happily include the link for you :) (oh by the way, I don't think the green and grey are exactly the same image) $\endgroup$ – Marcus Müller Mar 30 '17 at 8:53
  • $\begingroup$ I'm no image processing expert, but I remember Otsu's method being very cluster-oriented. Have you tried other methods, and if so, which? $\endgroup$ – Marcus Müller Mar 30 '17 at 8:54
  • $\begingroup$ @MarcusMüller imgur.com/a/FlTRR $\endgroup$ – Sarin Suriyakoon Mar 30 '17 at 8:58
  • $\begingroup$ @MarcusMüller Thank you for your respond :) I know only otsu. Do you have another methods name? I use python+opencv. $\endgroup$ – Sarin Suriyakoon Mar 30 '17 at 8:59
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    $\begingroup$ yeah, I think you might need to go much more for edge-based approaches. Notice how the background is brighter than the fingerprint on the left of the print, but darker on the right? That's a pretty unfortunate setting, because you can't even say that within a small local patch the fingerprint is the lighter/darker part. If you can change something about the way these photos are made, I'd definitely try to enhance the evenness of background irradiation $\endgroup$ – Marcus Müller Mar 30 '17 at 13:02
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Two steps,

  1. do a simple histogram equalization to make the brightness a little bit more even.

  2. Then use canny edge detector (as suggested by Marcus Muller).

matlab code:

I=imread('Your Image');
G1=rgb2gray(I);
G2=histeq(G1);
E2=edge(G2,'canny'); 
figure imshow(E2,[]);

Result (I hope it is what your are looking for):

enter image description here

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