Scale-Invariant Feature Transform
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A Scale-Invariant Feature Transform is a Computer Vision that ...
- See: Match Moving, Computer Vision, University of British Columbia, David Lowe (Computer Scientist), Object Recognition, Robotic Mapping, Image Stitching, 3D Modeling, Gesture Recognition, Video Tracking.
References
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/scale-invariant_feature_transform Retrieved:2017-10-13.
- The scale-invariant feature transform (SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was patented in the US by the University of British Columbia and published by David Lowe in 1999.
Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.
- The scale-invariant feature transform (SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was patented in the US by the University of British Columbia and published by David Lowe in 1999.