Frame selection based on a trained neural network
US11410038B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 17, 2021 |
| Grant date | Aug 9, 2022 |
| Priority date | — |
| Expiry date | Mar 17, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/171
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Various embodiments describe frame selection based on training and using a neural network. In an example, the neural network is a convolutional neural network trained with training pairs. Each training pair includes two training frames from a frame collection. The loss function relies on the estimated quality difference between the two training frames. Further, the definition of the loss function varies based on the actual quality difference between these two frames. In a further example, the neural network is trained by incorporating facial heatmaps generated from the training frames and facial quality scores of faces detected in the training frames. In addition, the training involves using a feature mean that represents an average of the features of the training frames belonging to the same frame collection. Once the neural network is trained, a frame collection is input thereto and a frame is selected based on generated quality scores.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.