Object detection using cascaded convolutional neural networks
US9418319B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 21, 2014 |
| Grant date | Aug 16, 2016 |
| Priority date | — |
| Expiry date | Dec 9, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V40/172
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Different candidate windows in an image are identified, such as by sliding a rectangular or other geometric shape of different sizes over an image to identify portions of the image (groups of pixels in the image). The candidate windows are analyzed by a set of convolutional neural networks, which are cascaded so that the input of one convolutional neural network layer is based on the input of another convolutional neural network layer. Each convolutional neural network layer drops or rejects one or more candidate windows that the convolutional neural network layer determines does not include an object (e.g., a face). The candidate windows that are identified as including an object (e.g., a face) are analyzed by another one of the convolutional neural network layers. The candidate windows identified by the last of the convolutional neural network layers are the indications of the objects (e.g., faces) in the image.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.