Name
Title | ||
|---|---|---|
Object Recognition With an Elastic Net-Regularized Hierarchical MAX Model of the Visual Cortex. |
Abstract | ||
|---|---|---|
The human visual cortex has evolved to determine efficiently objects from within a scene. Hierarchical MAX (HMAX) is an object recognition model which has been inspired by the visual cortex, and sparse coding, which is a characteristic of neurons in the visual cortex, was previously integrated into the HMAX model for improved performance. In this study, in order to further enhance recognition accu... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LSP.2016.2582541 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Brain modeling,Feature extraction,Visualization,Encoding,Computational modeling,Dictionaries,Object recognition | Pattern recognition,Visual cortex,Neural coding,Computer science,Elastic net regularization,Visualization,Lasso (statistics),Feature extraction,Artificial intelligence,Encoding (memory),Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
23 | 8 | 1070-9908 |
Citations | PageRank | References |
2 | 0.36 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ali Alameer | 1 | 3 | 1.11 |
| Ghazal Ghazaei | 2 | 2 | 0.36 |
| Patrick Degenaar | 3 | 56 | 17.02 |
| Jonathon A. Chambers | 4 | 56 | 6.96 |
| Kianoush Nazarpour | 5 | 75 | 19.08 |