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
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