A Stochastic Flow for Feature Extraction
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Abstract
In recent years evolution of level sets of two-dimensional
functions or images in time through a partial differential
equation has emerged as an important tool in image processing.
Curve evolutions, which may be viewed as an evolution
of a single level curve, has been applied to a wide variety
of problems such as smoothing of shapes, shape analysis
and shape recovery. We give a stochastic interpretation of
the basic curve smoothing equation, the so called geometric
heat equation, and show that this evolution amounts
to a rotational diffusion movement of the particles along
the contour. Moreover, assuming that a priori information
about the orientation of objects to be preserved is known,
we present new flows which amount to weighting the geometric
heat equation nonlinearly as a function of the angle
of the normal to the curve at each point.
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2000-06
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Proceedings