Name
Abstract | ||
|---|---|---|
Most prior approaches to the problem of stereoscopic 3D (S3D) visual discomfort prediction (VDP) have focused on the extraction of perceptually meaningful handcrafted features based on models of visual perception and of natural depth statistics. Toward advancing performance on this problem, we have developed a deep learning-based VDP model named deep visual discomfort predictor (DeepVDP). The Deep... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2018.2851670 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Visualization,Feature extraction,Training,Three-dimensional displays,Computational modeling,Predictive models,Tuning | Computer vision,Pattern recognition,Convolutional neural network,Stereoscopy,Visualization,Supervised learning,Feature extraction,Visual Discomfort,Artificial intelligence,Deep learning,Mathematics,Visual perception | Journal |
Volume | Issue | ISSN |
27 | 11 | 1057-7149 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hee-Seok Oh | 1 | 137 | 17.59 |
| Sewoong Ahn | 2 | 20 | 4.49 |
| Sanghoon Lee | 3 | 740 | 97.47 |
| Alan Conrad Bovik | 4 | 2284 | 75.56 |