Name
Title | ||
|---|---|---|
A nonparametric statistical method for image segmentation using information theory and curve evolution. |
Abstract | ||
|---|---|---|
In this paper, we present a new 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 level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Futhermore, our method, which does not require any training, performs as good as methods based on training. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TIP.2005.854442 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
level-set methods,region label,curve evolution,nonparametric density estimation.,image region,image segmentation,real image,informa- tion theory,associated gradient flow,information theory,index terms—curve evolution,region boundary,image segmentation problem,segmentation problem,nonparametric statistical method,image pixel intensity,information-theoretic optimization problem,statistical distributions,indexing terms,level set method,mutual information,probability density,gradient flow | Scale-space segmentation,Pattern recognition,Segmentation-based object categorization,Image processing,Image segmentation,Artificial intelligence,Region growing,Mutual information,Real image,Contextual image classification,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 10 | 1057-7149 |
Citations | PageRank | References |
102 | 3.93 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junmo Kim | 1 | 102 | 3.93 |
| J. W. Fisher, III | 2 | 147 | 7.30 |
| Anthony Yezzi | 3 | 111 | 5.85 |
| Müjdat Çetin | 4 | 1342 | 112.26 |
| Alan S. Willsky | 5 | 7466 | 847.01 |