Scale-invariant feature transform (SIFT) is an algorithm in computer vision to detect and describe local features in images. This tag is for programming questions related to SIFT implementation or programs that use SIFT algorithm for computer vision applications.
Scale-invariant feature transform (SIFT) is an algorithm in computer vision to detect and describe local features in images, introduced by prof. David Lowe in 2004.
The local features extracted from an image with SIFT can then be used to identify an object from that image. This visualization illustrates homologous SIFT keypoints in two separate pictures of the same magazine cover:
Applications of SIFT include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, and match moving.
Algorithm Steps
Scale-space extrema detection
Keypoint localization
Interpolation of nearby data for accurate position
Discarding low-contrast keypoints
Eliminating edge responses
Orientation assignment
Keypoint descriptor
More information can be obtained here