Autoregression image abnormity detection method of enhancing latent space based on memory
US12100200B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 30, 2021 |
| Grant date | Sep 24, 2024 |
| Priority date | — |
| Expiry date | Oct 21, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/52
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present application discloses an autoregression image abnormity detection method of enhancing a latent space based on memory, which belongs to the field of abnormity detection in computer vision. The present application comprises: selecting a training data set; constructing a network structure of an autoregression model of enhancing a latent space based on memory; preprocessing the training data set; initializing the autoregression model of enhancing a latent space based on memory; training the autoregression model of enhancing a latent space based on memory; verifying the model on the selected data set, and using the trained model to judge whether the input image is an abnormal image. In the present application, a prior distribution is not needed to be set such that the distribution of the data itself will not be destroyed, and it can prevent the model from reconstructing abnormal images, and ultimately can better judge abnormal images.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.