Name
Title | ||
|---|---|---|
A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity. |
Abstract | ||
|---|---|---|
Techniques that integrate neuroscience and information science benefit both fields. Many related models have been proposed in computer vision; however, in general, the robustness and recognition precision are still key problems in object recognition models. In this paper, inspired by the process by which humans recognize objects and its biological mechanisms, a new integrated and dynamic framework... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCDS.2017.2749978 | IEEE Transactions on Cognitive and Developmental Systems |
Keywords | Field | DocType |
Feature extraction,Semantics,Visualization,Biological system modeling,Data mining,Training,Neurons | Visual processing,Pattern recognition,Computer science,Robustness (computer science),Feature (machine learning),Artificial intelligence,Semantic feature,Artificial neural network,Ambiguity,Semantics,Machine learning,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
10 | 2 | 2379-8920 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peijie Yin | 1 | 3 | 1.75 |
| Hong Qiao | 2 | 1147 | 110.95 |
| Wu Wei | 3 | 46 | 3.09 |
| Lu Qi | 4 | 9 | 5.87 |
| Yinlin Li | 5 | 63 | 5.41 |
| Shanlin Zhong | 6 | 11 | 1.53 |
| Bo Zhang | 7 | 328 | 42.62 |