DocumentCode :
2465145
Title :
Parametrically deformable contour models
Author :
Staib, Lawrence H. ; Duncan, James S.
Author_Institution :
Dept. of Electr. Eng., Yale Univ., New Haven, CT, USA
fYear :
1989
fDate :
4-8 Jun 1989
Firstpage :
98
Lastpage :
103
Abstract :
Segmentation using boundary finding is enhanced both by considering the boundary as a whole and by using model-based shape information. Flexible constraints, in the form of a probabilistic deformable model, are applied to the problem of segmenting natural objects whose diversity and irregularity of shape makes them poorly represented in terms of fixed features of forms. The parametric model is based on the elliptic Fourier decomposition of the boundary. The segmentation problem is solved as an optimization problem, where the best match between the boundary (as defined by the parameter vector) and the image data is found. Initial experimentation shows good results on a variety of images
Keywords :
Fourier analysis; optimisation; pattern recognition; picture processing; elliptic Fourier decomposition; feature extraction; model-based shape information; optimization; parameter vector; parametrically deformable contour model; pattern recognition; picture processing; segmentation; Computed tomography; Deformable models; Heuristic algorithms; Image segmentation; Information analysis; Marine vehicles; Parametric statistics; Radiology; Rubber; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1989. Proceedings CVPR '89., IEEE Computer Society Conference on
Conference_Location :
San Diego, CA
ISSN :
1063-6919
Print_ISBN :
0-8186-1952-x
Type :
conf
DOI :
10.1109/CVPR.1989.37834
Filename :
37834
Link To Document :
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