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A shape prior constraint for implicit active contours This publication appears in: Pattern Recognition Letters Authors: W. Liu, Y. Shang, X. Yang, R. Deklerck and J. Cornelis Volume: 32 Issue: 15 Pages: 1937-1947 Publication Date: Nov. 2011
Abstract: We present a shape prior constraint to guide the evolution of implicit active contours. Our method
includes three core techniques. Firstly, a rigid registration is introduced, using a line search method
within a level set framework. The method automatically finds the time step for the iterative optimization
processes. The order for finding the optimal translation, rotation and scale is derived experimentally. Secondly, a single reconstructed shape is created from a shape distribution of a previously acquired learning
set. The reconstructed shape is applied to guide the active contour evolution. Thirdly, our method balances
the impact of the shape prior versus the image guidance of the active contour. A mixed stopping
condition is defined based on the stationarity of the evolving curve and the shape prior constraint. Our
method is completely non-parametric and avoids taking linear combinations of non-linear signed distance
functions, which would cause problems because distance functions are not closed under linear
operations. Experimental results show that our method is able to extract the desired objects in several
circumstances, namely when noise is present in the image, when the objects are in slightly different
poses and when parts of the object are invisible in the image.
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