Refereed full papers (journals, book chapters, international conferences)

2002

@inproceedings{KFY2002,
	vgclass =	{refpap},
	author =	{Junmo Kim and John W. Fisher and Anthony Yezzi and
	M\"{u}jdat \c{C}etin and and Alan. S. Willsky},
	title =	{Nonparametric Methods for Image Segmentation using
	Information Theory and Curve Evolution},
	booktitle =	{Proceedings of the IEEE 2002 Conference on Image
	Processing (ICIP 2002)},
	address =	{Rochester, NY, USA},
	month =	{September~22--25},
	year =	{2002},
	url =	{http://ssg.mit.edu/\~{}mcetin/publications/kim_ICIP02_nonpar.pdf},
	abstract =	{In this paper, we present a novel information theoretic
	approach to image segmentation. We cast the segmentation problem as the
	maximization of the mutual information between the region labels and
	the image pixel intensities, subject to a constraint on the total
	length of the region boundaries. We assume that the probability
	densities associated with the image pixel intensities within each
	region are completely unknown a priori, and we formulate the problem
	based on nonparametric density estimates. Due to the nonparametric
	structure, our method does not require the image regions to have a
	particular type of probability distribution, and does not require the
	extraction and use of a particular statistic. We solve the
	information-theoretic optimization problem by deriving the associated
	gradient flows and applying curve evolution techniques. We use fast
	level set methods to implement the resulting evolution. The evolution
	equations are based on nonparametric statistics, and have an intuitive
	appeal. The experimental results based on both synthetic and real
	images demonstrate that the proposed technique can solve a variety of
	challenging image segmentation problems.},
}