The Scale Invariant Feature Transform (SIFT) is a method to detect distinctive, invariant image feature points, which easily can be matched between images to perform tasks such as object detection and recognition, or to compute geometrical transformations between images. The open-source SIFT library available here is implemented in C using the OpenCV open-source computer vision library and includes functions for computing SIFT features in images, matching SIFT features between images using kd-trees, and computing geometrical image transforms from feature matches using RANSAC. The library also includes functionality to import and work with image features from both David Lowe’s SIFT executable and the Oxford VGG’s affine covariant feature detectors.
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- Mainali, Pradip; Lafruit, Gauthier; Yang, Qiong; Geelen, Bert; Van Gool, Luc; Lauwereins, Rudy: SIFER: scale-invariant feature detector with error resilience (2013)