Editing Scale-invariant feature transform (section)

=== Panorama stitching ===
SIFT feature matching can be used in [[image stitching]] for fully automated [[panorama]] reconstruction from non-panoramic images. The SIFT features extracted from the input images are matched against each other to find ''k'' nearest-neighbors for each feature. These correspondences are then used to find ''m'' candidate matching images for each image. [[homography|Homographies]] between pairs of images are then computed using [[Random sample consensus|RANSAC]] and a probabilistic model is used for verification. Because there is no restriction on the input images, graph search is applied to find [[Connected component (graph theory)|connected components]] of image matches such that each connected component will correspond to a panorama. Finally for each connected component [[bundle adjustment]] is performed to solve for joint camera parameters, and the panorama is rendered using [[multi-band blending]]. Because of the SIFT-inspired object recognition approach to panorama stitching, the resulting system is insensitive to the ordering, orientation, scale and illumination of the images. The input images can contain multiple panoramas and noise images (some of which may not even be part of the composite image), and panoramic sequences are recognized and rendered as output.<ref name="Brown2003" />