Region competition: Unifying snakes, region growing, and Bayes/MDL
for multiband image segmentation
IEEE Transactions On Pattern Analysis And Machine Intelligence, 18 (9):
884-900 (September 1996)Abstract
We present a novel statistical and variational approach to image
segmentation based on a new algorithm named region competition. This
algorithm is derived by minimizing a generalized Bayes/MDL criterion
using the variational principle. The algorithm is guaranteed to converge
to a local minimum and combines aspects of snakes/balloons and region
growing. Indeed the classic snakes/balloons and region growing algorithms
can be directly derived from our approach. We provide theoretical
analysis of region competition including accuracy of boundary location,
criteria for initial conditions, and the relationship to edge detection
using filters. It is straightforward to generalize the algorithm
to multiband segmentation and we demonstrate it on gray level images,
color images and texture images. The novel color model allows us
to eliminate intensity gradients and shadows, thereby obtaining segmentation
based on the albedos of objects. It also helps detect highlight regions.
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