Name
Abstract | ||
|---|---|---|
Active contour models are widely used in extracting object boundaries. However, most of these methods usually fail to capture concave boundaries properly and impose high computational cost. In this paper, a new SOM-based active contour model which introduces the Conscience and Archiving mechanisms (CASOM) is proposed to extend the Batch SOM method and eliminate its deficiencies. The performance of the proposed method is evaluated by some experiments on a set of grayscale images. Experimental results are compared with those of the BSOM in terms of accuracy and convergence speed. The results reveal that compared to BSOM, the proposed method requires less computations for converging to the object boundaries and extracts the boundaries of complex objects more accurately, even in the presence of weak or broken edges. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICARCV.2010.5707907 | Control Automation Robotics & Vision |
Keywords | Field | DocType |
computer graphics,self-organising feature maps,SOM based active contour model,archiving mechanism,batch SOM method,computational cost,concave boundaries,conscience mechanism,object boundaries,Active contour model,archiving mechanism,conscience,self-organizing map | Active contour model,Convergence (routing),Computer vision,Computer science,Self-organizing map,Artificial intelligence,Computer graphics,Grayscale,Computation | Conference |
ISSN | ISBN | Citations |
2474-2953 | 978-1-4244-7814-9 | 0 |
PageRank | References | Authors |
0.34 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fereshteh Sadeghi | 1 | 100 | 5.65 |
| Hamid Izadinia | 2 | 164 | 11.16 |
| Reza Safabakhsh | 3 | 505 | 31.43 |