Title :
A statistical approach to snakes for bimodal and trimodal imagery
Author :
Yezzi, Anthony, Jr. ; Tsai, Andy ; Willsky, Alan
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA
Abstract :
We describe a new region based approach to active contours for segmenting images composed of two or three types of regions characterizable by a given statistic. The essential idea is to derive curve evolutions which separate two or more valves of a pre-determined set of statistics computed over geometrically determined subsets of the image. Both global and local image information is used to evolve the active contour. Image derivatives, however, are avoided, thereby giving rise to a further degree of noise robustness compared to most edge based snake algorithms
Keywords :
computational geometry; edge detection; image segmentation; statistical analysis; active contours; bimodal imagery; curve evolutions; edge based snake algorithms; geometrically determined subsets; global image information; image derivatives; image segmentation; local image information; noise robustness; region based approach; statistical approach; trimodal imagery; Acoustic noise; Active contours; Clustering algorithms; Equations; Image edge detection; Image segmentation; Level set; Noise robustness; Statistics; Subcontracting;
Conference_Titel :
Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on
Conference_Location :
Kerkyra
Print_ISBN :
0-7695-0164-8
DOI :
10.1109/ICCV.1999.790317
