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Making Snakes Converge from Minimal Initialization

W. Neuenschwander, P. Fua, G. Székely and O. K├╝bler
ARPA Image Understanding Workshop
November 1994


In this paper, we propose a snake-based approach that lets a user specify only the distant end points of the curve he wishes to delineate without having to supply an almost complete polygonal approximation. We achieve much better convergence properties than those of traditional snakes by using the image information around these end points to provide boundary conditions and by introducing an optimization schedule that allows the snake to take image information into account first only near its extremities and then, progressively, towards its center. These snakes can be used to alleviate the often repetitive task practitioners have to face when segmenting images by abolishing the need to sketch a feature of interest in its entirety, that is, to perform a painstaking, almost complete, manual segmentation.

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  author = {W. Neuenschwander and P. Fua and G. Székely and O. K\"ubler},
  title = {Making Snakes Converge from Minimal Initialization},
  booktitle = {ARPA Image Understanding Workshop},
  year = {1994},
  month = {November},
  pages = {1627-1636},
  keywords = {grouping, segmentation, deformable, surface, deformable models, snakes}