Name
Abstract | ||
|---|---|---|
A number of segmentation techniques are compared with regard to their usefulness for region-based image and video fusion. In order to achieve this, a new multi-sensor data set is introduced containing a variety of infra-red, visible and pixel fused images together with manually produced "ground truth" segmentations. This enables the objective comparison of joint and unimodal segmentation techniques. A clear advantage to using joint segmentation over unimodal segmentation, when dealing with sets of multi-modal images, is shown. The relevance of these results to region-based image fusion is confirmed with task-based analysis and a quantitative comparison of the fused images produced using the various segmentation algorithms |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICIF.2006.301565 | Florence |
Keywords | Field | DocType |
image fusion,image segmentation,infra-red image,joint segmentation technique,multisensor data set,pixel fused image,region-based image fusion,task-based analysis,unimodal segmentation technique,video fusion,visible image,evaluation of segmentation,human segmentation,image fusion,multi-modal segmentation,region-based | Computer vision,Scale-space segmentation,Image fusion,Pattern recognition,Range segmentation,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Region growing,Artificial intelligence,Minimum spanning tree-based segmentation | Conference |
ISBN | Citations | PageRank |
0-9721844-6-5 | 2 | 0.44 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John J. Lewis | 1 | 226 | 9.44 |
| Stavri G. Nikolov | 2 | 246 | 12.64 |
| Cedric Nishan Canagarajah | 3 | 154 | 18.85 |
| David R. Bull | 4 | 1736 | 189.86 |
| Alexander Toet | 5 | 289 | 57.43 |