Name
Abstract | ||
|---|---|---|
We describe a gradient vector flow driven active shape method for model-based image segmentation. Active shape algorithm retain the shape feature of the interested object, and its performance relies heavily on initialization. Because of a lack of global regulation, the control points tends to be trapped in a local optimum in searching. Our proposed method uses the gradient vector flow of an image to guide the optimization process. The control points of an active shape are steered by the direction and the magnitude of gradient vectors. Our experiments demonstrated great improvement in finding the global optimum and resulting correct segmentation. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICME.2007.4285086 | ICME |
Keywords | Field | DocType |
optimization process,image segmentation,gradient vector flow,active shape algorithm,global optimum,noise shaping,principal component analysis,computer science,active shape model,process control | Computer vision,Point distribution model,Active shape model,Scale-space segmentation,Pattern recognition,Computer science,Active appearance model,Image segmentation,Vector flow,Artificial intelligence,Shape optimization,Heat kernel signature | Conference |
ISBN | Citations | PageRank |
1-4244-1017-7 | 3 | 0.38 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao-Hui Yuan | 1 | 534 | 75.44 |
| Balathasan Giritharan | 2 | 4 | 1.43 |
| JungHwan Oh | 3 | 520 | 44.87 |