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    <p>For me this is a classical problem in morphological filtering. We can see that as we improve the threshold we have different parts of the books that are visible.</p> <p><img src="https://i.stack.imgur.com/lcaFS.png" alt="Threshold at 100"> <img src="https://i.stack.imgur.com/9ve5I.png" alt="Threshold at 150"> and <img src="https://i.stack.imgur.com/2Yj6r.png" alt="Threshold at 172"></p> <p>Now some grayscale filtering based on the area/size of the grayscale components can provide with some good segmentation. This is called an area opening in morphological filtering: <a href="http://www.mathworks.fr/fr/help/images/ref/bwareaopen.html" rel="nofollow noreferrer">http://www.mathworks.fr/fr/help/images/ref/bwareaopen.html</a></p> <p>This basically removes connected components whose size/area is lesser than a certain parameter. Thus to see this in action here are a few filtered images of the thresholds above.</p> <p><img src="https://i.stack.imgur.com/S0Y9M.png" alt="Area opening and closing"></p> <p><img src="https://i.stack.imgur.com/Vb10A.png" alt="Another gray level"></p> <pre><code> x150 = I&gt;150; x100 = I&gt;100; x172 = I&gt;172; x150_op = bwareaopen(x150,80); x172_op = bwareaopen(x172,300); figure, imshow(~bwareaopen(x150_op,150); %area closing figure, imshow(~bwareaopen(x172_op,300); %area closing </code></pre> <p>Thus as we see we require a connected area opening filter which calculates connected grayscale componenets with a certain area. I think you can tune this for the books!</p> <p>Another library that provides the same operator and some examples: <a href="http://www.mmorph.com/mxmorph/html/morph/mmareaopen.html" rel="nofollow noreferrer">http://www.mmorph.com/mxmorph/html/morph/mmareaopen.html</a></p> <p>Best of luck!</p>
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    1. POsegmenting objects by their different intensity using MatLab
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    1. COThanks for your suggestion, I have already tried this actually, but my school supervisor stopped me. He said he wants to avoid any tuning or need for adjustment. The reason is so that a normal person is just able to snap an image, and the computer will tell him the number of books. This method however requires fine tuning every time the images (different number of books, different books size) changes.
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    2. COHey, Sure this is a valid comment and i agree that this requires some tuning of thresholds, but this is one time and should be robust. Here you can always pick the largest N grayscale components. More specifically here you need to count the number of largest grayscale components. And if there is threshold of area of noise components one can fix this. Also area filtering does not depend on the fact that you have books that are horizontal or slightly tilted, they are translationally and rotationally invariant.
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