A Fast Optimization Method for Level Set Segmentation

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A Fast Optimization Method for Level Set Segmentation
Abstract. Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent. Key words: image segmentation, level set meth...
Thord Andersson, Gunnar Läthén, Reiner
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where SCIA
Authors Thord Andersson, Gunnar Läthén, Reiner Lenz, Magnus Borga
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