Boundary Finding with Prior Shape and Smoothness Models
Yongmei Wang and Lawrence H. Staib
Yale University
IEEE Transactions on Pattern Analysis and Machine Intelligence,
22(7):738-743, 2000
Abstract
We propose a unified framework for boundary finding,
where a Bayesian formulation, based on prior knowledge
and the edge information of the input image (likelihood),
is employed. The prior
knowledge in our framework is based on principal component analysis of
four different covariance matrices
corresponding to independence, smoothness, statistical shape
and combined models, respectively.
Indeed, snakes, modal analysis, Fourier descriptors, and
point distribution models can be derived from or linked to our
approaches of different prior models.
When the true training set does not contain
enough variability to express the full range of deformations,
a mixed covariance
matrix uses a combined prior of the smoothness and statistical
variation modes. It adapts gradually to use more statistical modes of
variation as larger data sets are available.
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