Name
Abstract | ||
|---|---|---|
Images of nano-structures are often noisy. On the other hand, in many settings there is quite a lot of model knowledge regarding the observed structures. This paper proposes a method for segmenting an image using a geometric model of the the observed structure. The resulting segmentation is guaranteed to be globally optimal, for an explicitly specified score function. This property provides a great deal of robustness to the algorithm. The algorithm presented explores a pre-defined space of segmentations using a branch-and-bound algorithm. It eliminates those parts of the space that are provably poor and explores in further detail the more promising parts of the space. An example of a segmentation that can be obtained in this way is a straight line segmentation of an image into 2 regions that minimizes the intensity variation within the regions. Results showing extraction of specific nano-structures are presented. A trivial variation on the algorithm can find a maximum a-posteriori probability estimate of the segmentation when there exists an a-priori distribution over the segmentations and the objective function is interpreted as the likelihood of the image given the segmentation. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/CVPRW.2003.10017 | CVPR Workshops |
Field | DocType | Volume |
Computer vision,Branch and bound,Scale-space segmentation,Pattern recognition,Computer science,Range segmentation,Segmentation,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Minimum spanning tree-based segmentation | Conference | 2 |
Issue | ISSN | Citations |
1 | 1063-6919 | 4 |
PageRank | References | Authors |
0.49 | 3 | 1 |