Name
Title | ||
|---|---|---|
Algorithm And Implementation Of An Associative Memory For Oriented Edge Detection Using Improved Clustered Neural Networks |
Abstract | ||
|---|---|---|
Associative memories are capable of retrieving previously stored patterns given parts of them. This feature makes them good candidates for pattern detection in images. Clustered Neural Networks is a recently-introduced family of associative memories that allows a fast pattern retrieval when implemented in hardware. In this paper, we propose a new pattern retrieval algorithm that results in a dramatically lower error rate compared to that of the conventional approach when used in oriented edge detection process. This function plays an important role in image processing. Furthermore, we present the corresponding hardware architecture and implementation of the new approach in comparison with a conventional architecture in literature, and show that the proposed architecture does not significantly affect hardware complexity. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) | Associative property,Content-addressable memory,Edge detection,Computer science,Image processing,Electronic engineering,Artificial intelligence,Cluster analysis,Artificial neural network,Pattern recognition,Word error rate,Parallel computing,Hardware architecture |
DocType | ISSN | Citations |
Conference | 0271-4302 | 3 |
PageRank | References | Authors |
0.41 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robin Danilo | 1 | 3 | 1.08 |
| Hooman Jarollahi | 2 | 66 | 5.76 |
| Vincent Gripon | 3 | 210 | 27.16 |
| Philippe Coussy | 4 | 253 | 33.63 |
| Laura Conde-Canencia | 5 | 67 | 8.38 |
| Warren J. Gross | 6 | 1106 | 113.38 |