Eliminating edge points Such a point has large principal curvature across the edge but a small one in the perpendicular direction The principal curvatures can be calculated from a Hessian function The eigenvalues of H are proportional to the principal curvatures, so two eigenvalues shouldn’t diff too much
Orientation assignment Assign an orientation to each keypoint, the keypoint descriptor can be represented relative to this orientation and therefore achieve invariance to image rotation Compute magnitude and orientation on the Gaussian smoothed images
Orientation assignment A histogram is formed by quantizing the orientations into 36 bins; Peaks in the histogram correspond to the orientations of the patch; For the same scale and location, there could be multiple keypoints with different orientations;
Application: object recognition The SIFT features of training images are extracted and stored For a query image 1.Extract SIFT feature 2.Efficient nearest neighbor indexing 3.3 keypoints, Geometry verification
Extensions PCA-SIFT 1.Working on 41*41 patches 2.2*39*39 dimensions 3.Using PCA to project it to 20 dimensions
Surf Approximate SIFT Works almost equally well Very fast
Conclusions The most successful feature (probably the most successful paper in computer vision) A lot of heuristics, the parameters are optimized based on a small and specific dataset. Different tasks should have different parameter settings. Learning local image descriptors (Winder et al 2007): tuning parameters given their dataset. We need a universal objective function.
comments Ian: “ For object detection, the keypoint localization process can indicate which locations and scales to consider when searching for objects”. Mert : “uniform regions may be quite informative when detecting some types of ojbects, but SIFT ignore them ” Mani: “region detectors comparison ” Eamon:” whether one could go directly to a surface representation of a scene based on SIFT features “