![]() ![]() ![]() Weighted by the magnitude of their gradients, together with a gaussian kernel centered at the keypoint. The orientations of its neighbor points will be collected and used to build an orientation histogram, The results are like this: Orientation Assignmentįirst, we calculate gradient and orientation for every point in the Scale space.įor each keypoint detected by the previous procedure, Or on the edge (by thresholding principle curvature), to get more distinctive features. Reject the points with low contrast (by thresholding pixel value in DOG image) Then use parabolic interpolation to look for the accurate location of the extrema. In DOG Space, detect all the minimum and maximumīy comparing a pixel with its 26 neighbors in three directions. In each octave, calculate the differences of every two adjacent blurred images, to build a Difference-of-Gaussian space.ĭOG Space consists of grey images. This speeds up the computation significantly. The gaussian blur here is implemented by applying two 1-D convolutions, rather thanĪ 2-D convolution. Image, features will then have scale-invariant properties. Since features are then detected on different resized version of the original octaves), and each is then Gaussian-blurred The original image is resized in different sizes (AKA. Scale Space & DOG SpaceĪ Scale Space consisting of grey-scale images is built at the beginning. The procedure of the algorithm and some results are briefly described in this section. Lowe's SIFT algorithm is implemented in feature/. This is a summary of the algorithms I used to write OpenPano:Īn open source panorama stitcher. ![]()
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