Deformable contours: modeling and extraction
Lai, K.F.
Chin, R.T.
Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Nov 1995
Volume: 17,
Issue: 11
On page(s): 1084-1090
ISSN: 0162-8828
References Cited: 13
CODEN: ITPIDJ
INSPEC Accession Number: 5122088
Digital Object Identifier: 10.1109/34.473235
Current Version Published: 2002-08-06
Abstract
This paper considers the problem of modeling and extracting
arbitrary deformable contours from noisy images. We propose a global
contour model based on a stable and regenerative shape matrix, which is
invariant and unique under rigid motions. Combined with Markov random
field to model local deformations, this yields prior distribution that
exerts influence over a global model while allowing for deformations. We
then cast the problem of extraction into posterior estimation and show
its equivalence to energy minimization of a generalized active contour
model. We discuss pertinent issues in shape training, energy
minimization, line search strategies, minimax regularization and
initialization by generalized Hough transform. Finally, we present
experimental results and compare its performance to rigid template
matching
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