Object counting and instance segmentation using neural network architectures with image-level supervision
US10453197B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 18, 2019 |
| Grant date | Oct 22, 2019 |
| Priority date | — |
| Expiry date | Feb 18, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30242
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure relates to improved techniques for performing computer vision functions including common object counting and instance segmentation. The techniques described herein utilize a neural network architecture to perform these functions. The neural network architecture can be trained using image-level supervision techniques that utilize a loss function to jointly train an image classification branch and a density branch of the neural network architecture. The neural network architecture constructs per-category density maps that can be used to generate analysis information comprising global object counts and locations of objects in images.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.