Image assessment using deep convolutional neural networks
US9536293B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 30, 2014 |
| Grant date | Jan 3, 2017 |
| Priority date | — |
| Expiry date | Oct 2, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/454
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Deep convolutional neural networks receive local and global representations of images as inputs and learn the best representation for a particular feature through multiple convolutional and fully connected layers. A double-column neural network structure receives each of the local and global representations as two heterogeneous parallel inputs to the two columns. After some layers of transformations, the two columns are merged to form the final classifier. Additionally, features may be learned in one of the fully connected layers. The features of the images may be leveraged to boost classification accuracy of other features by learning a regularized double-column neural network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.