Unsupervised image-based anomaly detection using multi-scale context-dependent deep autoencoding gaussian mixture model
US10853937B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 16, 2019 |
| Grant date | Dec 1, 2020 |
| Priority date | — |
| Expiry date | Jun 28, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/06
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A false alarm reduction system is provided that includes a processor cropping each input image at randomly chosen positions to form cropped images of a same size at different scales in different contexts. The system further includes a CONDA-GMM, having a first and a second conditional deep autoencoder for respectively (i) taking each cropped image without a respective center block as input for measuring a discrepancy between a reconstructed and a target center block, and (ii) taking an entirety of cropped images with the target center block. The CONDA-GMM constructs density estimates based on reconstruction error features and low-dimensional embedding representations derived from image encodings. The processor determines an anomaly existence based on a prediction of a likelihood of the anomaly existing in a framework of a CGMM, given the context being a representation of the cropped image with the center block removed and having a discrepancy above a threshold.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.