Gradient vector flow: Difference between revisions

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'''Gradient vector flow''' ('''GVF'''), a [[computer vision]] framework introduced by Chenyang Xu and [[Jerry L. Prince]]<ref name=":1">{{ Cite conference | last1 = Xu | first1 = C. | last2 = Prince | first2 = J.L. | title = Gradient Vector Flow: A New External Force for Snakes | book-title = Proc. IEEE Conf. on Comp. Vis. Patt. Recog. (CVPR) | place = Los Alamitos | publisher = Comp. Soc. Press | pages = 66–71 | date = June 1997 | url = http://iacl.ece.jhu.edu/pubs/p087c.pdf}}</ref>

<ref name=":2">{{Cite journal | title = Snakes, Shapes, and Gradient Vector Flow| journal = IEEE Transactions on Image Processing | volume = 7| issue = 3| pages = 359-369| year = 1998| last1 = Xu | first1 = C.| last2 = Prince | first2 = J.L. | url = http://iacl.ece.jhu.edu/pubs/p084j.pdf}}</ref>, is the vector field that is produced by a process that smooths and diffuses an input vector field. It is usually used to create a vector field from images that points to object edges from a distance. It's is widely used in image analysis and computer vision applications for object tracking, shape recognition, [[Image segmentation|segmentation]], and [[edge detection]]. In particular, it's is commonly used in conjunction with [[active contour model]].

[[File:Gradient Vector Flow 3D Metasphere Example Result.png|thumb|300px|Results from Gradient Vector Flow algorithm applied to 3-D Metasphere data]]