Learning copy space using regression and segmentation neural networks
US10970599B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 15, 2018 |
| Grant date | Apr 6, 2021 |
| Priority date | — |
| Expiry date | Feb 1, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are disclosed for characterizing and defining the location of a copy space in an image. A methodology implementing the techniques according to an embodiment includes applying a regression convolutional neural network (CNN) to an image. The regression CNN is configured to predict properties of the copy space such as size and type (natural or manufactured). The prediction is conditioned on a determination of the presence of the copy space in the image. The method further includes applying a segmentation CNN to the image. The segmentation CNN is configured to generate one or more pixel-level masks to define the location of copy spaces in the image, whether natural or manufactured, or to define the location of a background region of the image. The segmentation CNN may include a first stage comprising convolutional layers and a second stage comprising pairs of boundary refinement layers and bilinear up-sampling layers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.