Name
Abstract | ||
|---|---|---|
A description is given of the development of a VLSI smart sensor which borrows some mechanisms used by the human eye to generate abstractions of simple line drawings which are invariant with respect to scale and position. Simulation results are presented to show that the representation generated is invariant with respect to the transformations mentioned above. It is shown that such a sensor is capable of real-time performance, yields a considerably higher resolution than conventional schemes, and is similar in some important respects to the human visual system. Two implementation schemes are described, and the extension of the method to gray-scale images is discussed |
Year | DOI | Venue |
|---|---|---|
1990 | 10.1109/IJCNN.1990.137961 | IJCNN |
Keywords | Field | DocType |
vlsi,computational geometry,computer vision,computerised pattern recognition,electric sensing devices,neural nets,abstractions,gray-scale images,human eye,invariant geometric object recognition,real-time performance,resolution,retinomorphic vlsi smart sensor,simple line drawings,visual system,real time,human visual system,object recognition | Human eye,Computer vision,Human visual system model,Computer science,Computational geometry,Invariant (mathematics),Artificial intelligence,Artificial neural network,Very-large-scale integration,Machine learning,Line drawings,Cognitive neuroscience of visual object recognition | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arun Rao | 1 | 48 | 8.29 |
| Akers, L.A. | 2 | 0 | 0.68 |