Cross-trained convolutional neural networks using multimodal images
US9633282B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 30, 2015 |
| Grant date | Apr 25, 2017 |
| Priority date | — |
| Expiry date | Oct 8, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/194
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of a computer-implemented method for training a convolutional neural network (CNN) that is pre-trained using a set of color images are disclosed. The method comprises receiving a training dataset including multiple multidimensional images, each multidimensional image including a color image and a depth image; performing a fine-tuning of the pre-trained CNN using the depth image for each of the plurality of multidimensional images; obtaining a depth CNN based on the pre-trained CNN, wherein the depth CNN is associated with a first set of parameters; replicating the depth CNN to obtain a duplicate depth CNN being initialized with the first set of parameters; and obtaining a depth-enhanced color CNN based on the duplicate depth CNN being fine-tuned using the color image for each of the plurality of multidimensional images, wherein the depth-enhanced color CNN is associated with a second set of parameters.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.