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copied!<p>There shpuld be quite a lot of research going on in this area, but unfortunately I have no good links to give.</p> <p>An idea, which might work but I have not tested, is to try to estimate the lighting variations and then remove that before thresholding (which is a better term than "binarization"). The problem is then moved from adaptive thresholding to finding a good lighting model.</p> <p>If you know anything about the light sources then you could of course build a model from that. </p> <p>Otherwise a quick hack that might work is to apply a really heavy low pass filter to your image (blur it) and then use that as your lighting model. Then create a difference image between the original and the blurred version, and threshold that.</p> <p><strong>EDIT</strong>: After quick testing, it appears that my "quick hack" is not really going to work at all. After thinking about it I am not very surprised either :)</p> <pre><code>I = someImage Ib = blur(I, 'a lot!') Idiff = I - Idiff It = threshold(Idiff, 'some global threshold') </code></pre> <p><strong>EDIT 2</strong> Got one other idea which could work depending on how your images are generated. Try estimating the lighting model from the first few rows in the image:</p> <ol> <li>Take the first N rows in the image</li> <li>Create a mean row from the N collected rows. You know have one row as your background model.</li> <li>For each row in the image subtract the background model row (the mean row).</li> <li>Threshold the resulting image.</li> </ol> <p>Unfortunately I am at home without any good tools to test this.</p>

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