![]() ![]() \(N_m\) number of matched feature points. Where \(I\) is source image \(I_t\) is destination or transformed image. We use repeatability measure which is defined as: ![]() To test performance of different descriptors. Evaluation criteria for different feature descriptors For example we can model the difference between two images to a set of transformations and run RANSAC to find best model that maximize correct matching. The basic idea of RANSAC algorithm is shown in the following flow chart. ![]() We will model the transformation of points in source image to destination one, and try to find an estimate of model parameters. Random sample consensus is an iterative method for estimation of parameters of a mathematical model. It is extremely expensive as we know any brute-force algorithm will guarantee getting a solution, but doesn’t guarantee getting optimal solution. In brute-force matcher we have to match descriptor of all features in an image to descriptors of all features in another image. Where \(v_1\) and \(v_2\) are two feature descriptors. So feature will be matched with another with minimum SSD value. The basic idea of feature matching is to calculate the sum square difference between two different feature descriptors (SSD). SIFT feature descriptor will be a vector of 128 element (16 blocks \(\times\) 8 values from each block).For each block get magnitude weighted angle histogram and normalize it (divide by total gradient magnitudes).Īngles (quantized to 8 angles ) based on its relevant gradient magnitude i.e (histogram of angle 0 = sum(all magnitudes with angle 0)).Divide this \(16 \times 16\) patch to sixteen \(4 \times 4\) blocks.subtract dominant direction from gradient angle.Locate dominant corner direction which is most probable angle (angle with max value in 36 bit angle histogram).Get the gradient angle of the window and Quantize them to 36 values (0, 10, 20, …, 360).Adjusting orientation (To be rotation invariant):.Get gradient magnitude and multiply it by a \(16 \times 16\) gaussian window of \(\sigma =1.5\).Extract a \(16 \times 16\) window centered by this point.For same iamge, it is not necessary for its corners to be localized at same scale.Īfter localization of a key-point in our scale space. It is achieved by comparing same corner with its neighbors of above and lower scales and select scale with maximum value. ![]() SIFT scale spaceĭifferent levels of image resolutions (pyramids levels)ĭifferent scales of window in each octave level (different \(\sigma\) of gaussian window)įor each key-point (corner) we need to find its best scale which have maximum value (cornerness measure). In SIFT we usually prefer DOG scale space which is an approximate of LOG and simpler in calculation. The basic idea to build scale space is shown in the following figure Image pyramids or image scale space is the proposed method to handle images in different scales. But it is a descriptor of detected corners of different image scales or image pyramids. Scale invariant feature descriptor (SIFT) is not a new way to find key-points or corners that is invariant to scale. Scale invariant feature descriptor (SIFT) Large corners needs large windows and smaller corners needs smaller windows. So size of the window will effect the detection of corners. It will be difficult to detect that corner so this feature point will not be recognized for all scales. In smaller image, it’s easy to detect that there is a corner, but what about same image in the large scale. Next figure shows two different scales of same image. Corner detectors are invariant for translation, illumination and rotation. Harris and FAST are two different corner detectors, we have discussed later.
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