Name
Title | ||
|---|---|---|
Sub-pattern texture recognition using intelligent focal-plane imaging sensor of small window-size |
Abstract | ||
|---|---|---|
In this paper we demonstrate how to use statistical evaluation for texture recognition in the case of window-size of the imaging focal-plane sensor being smaller than the pattern of the texture. The evaluation method is similar to the sub-pixel pattern recognition developed by the first author. We have reported in an earlier publication on the development of a new single-chip texture classifier smart-sensor system, whose main part is a cellular nonlinear network (CNN) VLSI chip. This architecture is very fast but it has a limited window-size. Now we show that this architecture can effectively recognize textures of periodicity larger than the window-size. As a result, we recognized 15 Brodatz-textures by using a 20 x 22 CNN chip with a 0.4% error-rate. (C) 1999 Elsevier Science B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0167-8655(99)00080-X | PATTERN RECOGNITION LETTERS |
Keywords | Field | DocType |
smart sensors,genetic algorithm,texture analysis,cellular nonlinear network,sub-pixel recognition,density estimation | Density estimation,Computer vision,Vlsi chip,Nonlinear system,Image sensor,Pattern recognition,Cardinal point,Computer science,Chip,Artificial intelligence,Classifier (linguistics),Genetic algorithm | Journal |
Volume | Issue | ISSN |
20 | 11-13 | 0167-8655 |
Citations | PageRank | References |
1 | 0.37 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tamás Szirányi | 1 | 152 | 26.92 |
| Attila Hanis | 2 | 1 | 0.37 |