Method for image segmentation using CNN
US11270447B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 10, 2020 |
| Grant date | Mar 8, 2022 |
| Priority date | — |
| Expiry date | Aug 5, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H50/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In a convolutional neural network (CNN) using an encoder-decoder structure for image segmentation, a multi-scale context aggregation module receives an encoded final-stage feature map from the encoder, and sequentially aggregates multi-scale contexts of this feature map from a global scale to a local scale to strengthen semantic relationships of contexts of different scales to improve segmentation accuracy. The multi-scale contexts are obtained by computing atrous convolution on the feature map for different dilation rates. To reduce computation, a channel-wise feature selection (CFS) module is used in the decoder to merge two input feature maps. Each feature map is processed by a global pooling layer followed by a fully connected layer or a 1×1 convolutional layer to select channels of high activation. By subsequent channel-wise multiplication and elementwise summation, only channels with high activation in both feature maps are preserved and enhanced in the merged feature map.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.