Multi-scale deep learning system
US10521699B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 12, 2017 |
| Grant date | Dec 31, 2019 |
| Priority date | — |
| Expiry date | Jan 28, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2210/12
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system for identifying objects in an image is provided. The system identifies segments of an image that may contain objects. For each segment, the system generates a segment score by inputting to a multi-scale neural network windows of multiple scales that include the segment that have been resampled to a fixed window size. A multi-scale neural network includes a feature extracting convolutional neural network (“feCNN”) for each scale and a classifier that inputs each feature of each feCNN. The segment score indicates whether the segment contains an object. The system generates a pixel score for pixels of the image. The pixel score for a pixel indicates that that pixel is within an object based on the segment scores of segments that contain that pixel. The system then identifies the object based on the pixel scores of neighboring pixels.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.