Title :
Genetic algorithms for active contour optimization
Author :
MacEachern, Leonard A. ; Manku, Tajinder
Author_Institution :
Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont., Canada
fDate :
31 May-3 Jun 1998
Abstract :
Genetic Algorithm Snakes (GA-Snakes) are introduced as a new method of contour-based segmentation. The new algorithm exhibits several key features: a low order of complexity, the ability to handle arbitrary constraints, operation in discrete space, avoidance of higher-order derivatives, possible parallel computation, low/fixed storage requirements, the ability to handle large search spaces comfortably, and the ability to escape local minima and to handle non-convex search spaces. The resulting algorithmic complexity is of order O(nλG), where n is the number of control points for the contour, λ is the number of individuals per generation, and G is the number of generations considered
Keywords :
edge detection; genetic algorithms; image segmentation; active contour optimization; algorithmic complexity; arbitrary constraints; contour-based segmentation; control points; discrete space; fixed storage requirements; genetic algorithm snakes; higher-order derivatives; local minima; order of complexity; parallel computation; search spaces; Active contours; Computer vision; Concurrent computing; Cost function; Genetic algorithms; Image edge detection; Image processing; Image segmentation; Optimization methods; Search problems;
Conference_Titel :
Circuits and Systems, 1998. ISCAS '98. Proceedings of the 1998 IEEE International Symposium on
Conference_Location :
Monterey, CA
Print_ISBN :
0-7803-4455-3
DOI :
10.1109/ISCAS.1998.698801