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Estimate white background

asked 2016-02-10 09:36:16 -0500

podlipensky gravatar image

updated 2016-02-10 10:12:57 -0500


I have image with white uneven background (due to lighting). I'm trying to estimate background color and transform image into image with true white background. For this I estimated white color for each 15x15 pixels block based on its luminosity. So I've got the following map (on the right): image description

Now I want to interpolate color so it will be more smooth transition from 15x15 block to neighboring block, plus I want it to eliminate outliers (pink dots on left hand side). Could anyone suggest good technique/algorithm for this? (Ideally within OpenCV library, but not necessary)

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LBerger gravatar imageLBerger ( 2016-02-10 14:25:54 -0500 )edit

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answered 2016-02-11 08:33:38 -0500

Simplest solution, convert to grayscale and do a OTSU thresholding, which will be between the letters and the background. Then simply replace the background with plain white color!

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OTSU thresholding won't work perfectly with illumination, i.e. if some drawing is light on a photo due to illumination, it will be filtered out by OTSU thresholding since it will be done in grayscale mode. Any other ideas?

podlipensky gravatar imagepodlipensky ( 2016-02-21 12:34:18 -0500 )edit

answered 2016-02-10 10:06:39 -0500

jeanpat gravatar image

updated 2016-02-10 13:40:29 -0500

What about a top-hat filtering? From an ipython console:

kernel18 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(18,18))
filtered = cv2.morphologyEx(image, cv2.MORPH_TOPHAT, kernel18)

The image may has to be converted into a greyscaled image.

It's possible to get something like this image with a smaller kernel (clik on the link)

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If I convert image to grayscale, I'll loose an ability to detect outliers, but let me take a closer look to this method...

podlipensky gravatar imagepodlipensky ( 2016-02-10 10:14:01 -0500 )edit

From your image:

board = mh.imread('/home/jeanpat/Images/paperboard.jpg',-1)
cut = board[40:,10:1000]
cut = cut.max()-cut
print cut.shape, cut.max()
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(15,15))
cut_f = cv2.morphologyEx(cut,cv2.MORPH_TOPHAT, kernel)

(mh is a shortcut for mahotas, but it should possible to load the image with cv2 itself) link text

jeanpat gravatar imagejeanpat ( 2016-02-10 10:38:46 -0500 )edit

@jeanpat, unfortunately the operation above makes resulting image black. I believe this is due to erosion. But overall approach might work just fine if I can use reverse-erosion... Playing with it.

podlipensky gravatar imagepodlipensky ( 2016-02-10 12:54:01 -0500 )edit

A top hat with a 15x15 structuring element succeeds in extracting the characters (see the link)

jeanpat gravatar imagejeanpat ( 2016-02-10 13:42:31 -0500 )edit
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Asked: 2016-02-10 09:36:16 -0500

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Last updated: Feb 11 '16