Name
Abstract | ||
|---|---|---|
We developed a neural net architecture for segmenting complex images, i.e., to localize two-dimensional geometrical shapes in a scene, without prior knowledge of the objects' positions and sizes. A scale variation is built into the network to deal with varying sizes. This algo(cid:173) rithm has been applied to video images of railroad cars, to find their identification numbers. Over 95% of the characlers were located correctly in a data base of 300 images, despile a large variation in light(cid:173) ing conditions and often a poor quality of the characters. A part of the network is executed on a processor board containing an analog neural net chip (Graf et aI. 1991). while the rest is implemented as a software model on a workstation or a digital signal processor. |
Year | Venue | Keywords |
|---|---|---|
1991 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 4 | image segmentation |
Field | DocType | Volume |
Computer vision,Digital signal processor,Computer science,Workstation,Image segmentation,Chip,Software,Neural net architecture,Artificial intelligence,Artificial neural network,Scale variation | Conference | 4 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hans Peter Graf | 1 | 724 | 79.48 |
| Craig R. Nohl | 2 | 39 | 11.46 |
| Jan Ben | 3 | 115 | 20.89 |