Extract interest point descriptors
[ returns
extracted feature vectors, also known as descriptors, and their corresponding
locations, from a binary or intensity image.features,validPoints]
= extractFeatures(I,points)
The function derives the descriptors from pixels surrounding
an interest point. The pixels represent and match features specified
by a single-point location. Each single-point specifies the center
location of a neighborhood. The method you use for descriptor extraction
depends on the class of the input points.
[ uses
additional options specified by one or more features,validPoints]
= extractFeatures(I,points,Name,Value)Name,Value pair
arguments.
[1] G. Bradski and A. Kaehler, Learning OpenCV : Computer Vision with the OpenCV Library, O'Reilly, Sebastopol, CA, 2008.
[2] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008
[3] Bay, Herbert, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346--359, 2008.
[4] Alahi, Alexandre, Ortiz, Raphael, and Pierre Vandergheynst, "FREAK: Fast Retina Keypoint", IEEE Conference on Computer Vision and Pattern Recognition, 2012.
[5] Alcantarilla, P.F., A. Bartoli, and A.J. Davison. "KAZE Features", ECCV 2012, Part VI, LNCS 7577 pp. 214, 2012
binaryFeatures | detectBRISKFeatures | detectFASTFeatures | detectHarrisFeatures | detectKAZEFeatures | detectMinEigenFeatures | detectMSERFeatures | detectORBFeatures | detectSURFFeatures | extractHOGFeatures | extractLBPFeatures | KAZEPoints | matchFeatures | MSERRegions | ORBPoints | SURFPoints