Leaf segmentation and tracking using probabilistic parametric active contours

Authors:
Jonas De Vylder;Daniel Ochoa;Wilfried Philips;Laury Chaerle;Dominique Van Der Straeten
Affiliations:
Department of Telecommunications and Information Processing, IBBT, Image Processing and Interpretation, Ghent University;Department of Telecommunications and Information Processing, IBBT, Image Processing and Interpretation, Ghent University and Facultad de Ingenieria en Electricidad y Computacion, Escuela Superior ...;Department of Telecommunications and Information Processing, IBBT, Image Processing and Interpretation, Ghent University;Department of Physiology, Laboratory of Functional Plant Biology, Ghent University;Department of Physiology, Laboratory of Functional Plant Biology, Ghent University
Venue:
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
Year:
2011

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Abstract

Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is generally a linear combination of a data fit term and a regularization term. This energy function can be adjusted to exploit the intrinsic object and image features. This can be done by changing the weighting parameters of the data fit and regularization term. There is, however, no rule to set these parameters optimally for a given application. This results in trial and error parameter estimation. In this paper, we propose a new active contour framework defined using probability theory. With this new technique there is no need for ad hoc parameter setting, since it uses probability distributions, which can be learned from a given training dataset.