Name
Abstract | ||
|---|---|---|
In this paper, we integrate image segmentation and object recognition into one unified process. The process starts from a seed region produced by the initial segmentation. Then it is evolved through alternating shape context based recognition and smaller scale image segmentation, until an object is found. In recognition, the thin plate spline (TPS) transformation is employed to locate the difference between the seed region and the defined model, and guide the subsequent evolution process. Experimental results demonstrate the advantages achieved by the combination of hierarchical segmentation and multi-scale shape contexts based recognition. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CIT.2008.4594783 | CIT |
Keywords | Field | DocType |
seed region,image segmentation,thin plate spline transformation,hierarchical segmentation,feature extraction,object recognition,shape context based recognition,splines (mathematics),shape,brightness,spline,histograms,image recognition,unified process,thin plate spline,computer science,pixel,lighting | Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Segmentation-based object categorization,Feature extraction,Image segmentation,Artificial intelligence,Region growing,Shape context,Cognitive neuroscience of visual object recognition | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4244-2358-3 |
Citations | PageRank | References |
1 | 0.38 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fenglei Yang | 1 | 8 | 2.50 |
| Yue Lu | 2 | 1617 | 101.51 |
| Ye Duan | 3 | 188 | 22.38 |