Algorithms that find corners, edges, lines and distinctive patches in an image so that a program can match, recognise or track objects. The Harris corner detector and the Hough transform find corners and straight lines, SIFT, SURF and ORB describe local features that survive changes of scale and rotation, the histogram of oriented gradients captures the outline of shapes, and non-maximum suppression keeps only the strongest of nearby responses.
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Image Feature Detection Algorithms
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Harris corner detector (Wikipedia)
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The Harris corner detector is a corner detection operator that is commonly used in computer vision algorithms to extract corners and infer features of an image.
Scale-invariant feature transform (Wikipedia)
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The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999.
Speeded up robust features (Wikipedia)
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In computer vision, speeded up robust features (SURF) is a local feature detector and descriptor, with patented applications.
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