Name
Abstract | ||
|---|---|---|
Many computer vision techniques, above all for structure from motion problems, require a segmentation of the images captured by one or more cameras. This paper deals with the segmentation based on the motion information, but can be easily extended to other cases (color, texture and so on). A new neural network, the EXIN Segmentation Neural Network (EXIN SNN) is here introduced. It is incremental, self-organizing and considers its task as the solution of a pattern recognition problem. This original approach overcomes the limits of the traditional segmentation techniques, namely the need of a spatial support for the image objects and the translation parallel to the image plane for the objects in the scene. Examples are given both for synthetic and real images. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1023/A:1020970617241 | Appl. Intell. |
Keywords | Field | DocType |
motion segmentation,neural networks,self organization,structure from motion | Structure from motion,Scale-space segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Time delay neural network,Artificial intelligence,Computer vision,Pattern recognition,Segmentation,Image plane,Real image,Machine learning | Journal |
Volume | Issue | ISSN |
18 | 1 | 1573-7497 |
Citations | PageRank | References |
8 | 0.73 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giansalvo Cirrincione | 1 | 121 | 13.13 |
| Maurizio Cirrincione | 2 | 124 | 16.58 |