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copied!<blockquote> <ol start="2"> <li>Can we apply SURF to color, HSV space?</li> </ol> </blockquote> <p>I didn't test it, but as far as I know, SIFT and SURF use quite (in principle) similar detection techniques:</p> <p>SIFT detector uses the <a href="https://docs.gimp.org/en/plug-in-dog.html" rel="nofollow">Difference-of-Gaussian (DoG)</a> technique to efficiently approximate the <a href="http://homepages.inf.ed.ac.uk/rbf/HIPR2/log.htm" rel="nofollow">Laplacian-of-Gaussian (LoG)</a>, which both are <a href="http://www.learnopencv.com/blob-detection-using-opencv-python-c/" rel="nofollow">Blob Detection</a> techniques.</p> <p>SURF detector uses <em>box-filters/box-blurs</em> of arbitrary size to compute (or approximate?) <em>The determinant of the Hessian</em> which is a <em>Blob Detection</em> technique.</p> <p>Both methods use some strategy to compute those blobs in multiple scales (SIFT: DoG-Pyramid; SURF: integral images to scale the filter sizes). At the end, both methods detect <em>blobs</em> in the given 2D array.</p> <p>So if SIFT can detect good features in your (H)SV channels, SURF should be able to do the same because in principle they both detect blobs. What you will do is detecting blobs in the hue/saturation/value channel:</p> <ul> <li><p><em>hue-blobs</em>: regions of similar color-tone which are surrounded by different (all higher or all lower) color-tones;</p></li> <li><p><em>saturation-blobs</em>: regions of... yea of what? no idea how to interpret that;</p></li> <li><p><em>value-blobs</em>: should give very similar results to the grayimage converted RGB image's blobs.</p></li> </ul> <p>One thing to add: I'm just handling the <em>detector</em>! No idea how SIFT/SURF <em>description</em> is influenced by color data.</p>

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