Heterogeneous convolutional neural network for multi-problem solving
US10990820B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 5, 2019 |
| Grant date | Apr 27, 2021 |
| Priority date | — |
| Expiry date | Apr 21, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/582
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A heterogeneous convolutional neural network (HCNN) system includes a visual reception system generating an input image. A feature extraction layer (FEL) portion of convolutional neural networks includes multiple convolution, pooling and activation layers stacked together. The FEL includes multiple stacked layers, a first set of layers learning to represent data in a simple form including horizontal and vertical lines and blobs of colors. Following layers capture more complex shapes such as circles, rectangles, and triangles. Subsequent layers pick up complex feature combinations to form a representation including wheels, faces and grids. The FEL portion outputs data to each of: a first sub-network which performs a first task of object detection, classification, and localization for classes of objects in the input image to create a detected object table; and a second sub-network which performs a second task of defining a pixel level segmentation to create a segmentation data set.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.