Active contour model, also called snakes, is a framework in computer vision introduced by Michael Kass, Andrew Witkin, and Demetri Terzopoulos[1] for delineating an object outline from a possibly noisy 2D image. The snakes model is popular in computer vision, and snakes are widely used in applications like object tracking, shape recognition, segmentation, edge detection and stereo matching.
A snake is an energy minimizing, deformable spline influenced by constraint and image forces that pull it towards object contours and internal forces that resist deformation. Snakes may be understood as a special case of the general technique of matching a deformable model to an image by means of energy minimization.[1] In two dimensions, the active shape model represents a discrete version of this approach, taking advantage of the point distribution model to restrict the shape range to an explicit domain learnt from a training set.
Snakes – active deformable models
Snakes do not solve the entire problem of finding contours in images, since the method requires knowledge of the desired contour shape beforehand. Rather, they depend on other mechanisms such as interaction with a user, interaction with some higher level image understanding process, or information from image data adjacent in time or space.
^ abKass, M.; Witkin, A.; Terzopoulos, D. (1988). "Snakes: Active contour models" (PDF). International Journal of Computer Vision. 1 (4): 321. CiteSeerX 10.1.1.124.5318. doi:10.1007/BF00133570. S2CID 12849354. Archived from the original (PDF) on 2016-01-12. Retrieved 2015-08-29.
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