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copied!<p>Shai's recommendation to calculate the mean along each horizontal row of the image can help identify books. That method would have certain limitations if the books aren't flat, aren't horizontal, or have similar colors. However, rather than focusing on the clumps of similar brightness, you might instead consider using the horizontal mean/median or the like as a means to detect possible air gaps between books. </p> <p>I wouldn't bother much with working in binary images. Too fiddly, and not very robust. You can find a solution for certain images, but could spend heaps of time trying to find a solution for all images. The clustering of grayscale intensities of this image is very important, and you don't want to throw that data away. Thresholding before binarization can be a messy and unsatisfying business.</p> <p>Are you familiar with the mean shift algorithm? It's a clustering algorithm similar to k-means clustering, but doesn't require knowledge of the number of items (k) in advance. This could help you cluster the data in the original grayscale image and find that there are three large, distinct clumps of reasonably consistent grayscale value.</p> <p><a href="https://stackoverflow.com/questions/4831813/image-segmentation-using-mean-shift-explained">Image Segmentation using Mean Shift explained</a></p> <p><a href="http://docs.opencv.org/trunk/doc/py_tutorials/py_video/py_meanshift/py_meanshift.html" rel="nofollow noreferrer">http://docs.opencv.org/trunk/doc/py_tutorials/py_video/py_meanshift/py_meanshift.html</a></p> <p><a href="http://saravananthirumuruganathan.wordpress.com/2010/04/01/introduction-to-mean-shift-algorithm/" rel="nofollow noreferrer">http://saravananthirumuruganathan.wordpress.com/2010/04/01/introduction-to-mean-shift-algorithm/</a></p>

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