Title :
Pixel-level snakes
Author :
Vilarino, David L. ; Cagello, D. ; Pardo, Xose M. ; Brea, Victor M.
Author_Institution :
Dept. of Electron., Santiago de Compostela Univ., Spain
Abstract :
An alternative to classical image segmentation based on active contour techniques is discussed. The approach is based on deformable contours which evolve until reaching a final desired location. The contour shift is guided by external information from the image under consideration which attracts them towards the target characteristics (intensity, extremes, edges,...) and by internal forces which try to maintain the smoothness of the contour curve. These forces act on each pixel of the contours, resulting in a high degree of freedom for the contour evolution and provide a high flexibility for the evolution dynamics of the snakes which allows the solution of complex tasks as is the case for topologic transformations. This, along with the use of only local information will allow the algorithm implementation into an array of processors leading towards “pixel-level” contour processing
Keywords :
cellular neural nets; distributed processing; image segmentation; image thinning; topology; active contour techniques; contour curve; contour evolution; deformable contours; edges; evolution dynamics; extremes; intensity; local information; pixel-level contour processing; pixel-level snakes; topologic transformations; Active contours; Anatomical structure; Biomedical image processing; Cellular neural networks; Computer science; Deformable models; Force control; Image segmentation; Signal to noise ratio; Solid modeling;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.905419