老外经典SIFT算法PPT.pptVIP

Scale Invariant Feature Transform Tom Duerig Why do we care about matching features? Object Recognition Wide baseline matching Tracking/SFM We want invariance!!! Good features should be robust to all sorts of nastiness that can occur between images. Types of invariance Illumination Types of invariance Illumination Scale Types of invariance Illumination Scale Rotation Affine Types of invariance Illumination Scale Rotation Affine Full Perspective How to achieve illumination invariance The easy way (normalized) Difference based metrics (random tree, Haar, and sift) How to achieve scale invariance Pyramids Divide width and height by 2 Take average of 4 pixels for each pixel (or Gaussian blur) Repeat until image is tiny Run filter over each size image and hope its robust Scale Space (DOG method) Pyramids How to achieve scale invariance Pyramids Scale Space (DOG method) Pyramid but fill gaps with blurred images Like having a nice linear scaling without the expense Take features from differences of these images If the feature is repeatably present in between Difference of Gaussians it is Scale Invariant and we should keep it. Differences Of Gaussians Rotation Invariance Rotate all features to go the same way in a determined manner Take histogram of Gradient directions (36 in paper for 1 every 10 degrees) Rotate to most dominant (maybe second if its good enough, sub-Bin accuracy) Rotation Invariance Affine Invariance Easy way: Warp your training and hope Fancy way: design your feature itself to be robust against affine transformations (SIFT method) Actual SIFT features Remember the gradient histograms we used for rotation invariance? Same theory, except keep N2 histograms (4 shown, 16 used) Note, use weighted contributions to avoid edge nastiness SIFT algorithm overview Get tons of points from maxima+minima of DOGS Threshold on simple contrast (low contrast is generally less reliable than high for feature points) Threshold based on principal curvatures (technical