Parameter training method for a convolutional neural network and method for detecting items of interest visible in an image and for associating items of interest visible in an image
US11302114B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 2, 2019 |
| Grant date | Apr 12, 2022 |
| Priority date | — |
| Expiry date | Apr 27, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/625
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This invention concerns a method of learning parameters of a convolutional neural network (CNN) through data processing means (11a, 11b, 11c) of at least one server (1a, 1b, 1c), for detecting items of interest visible in images, from at least one image learning database in which said items of interest, as well as characteristic geometric structures are already annotated, the CNN comprising an encoding layer for generating a representation vector of the detected items of interest, the method being characterized in that said representation vector comprises, for at least a first item of interest category to be detected, at least one descriptive value of at least a characteristic geometric structure of said first item of interest category.The present invention also relates to a process for detecting items of interest visible in an image and a method for associating items of interest visible in an image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.