Invention Grant
- Patent Title: Unsupervised image-based anomaly detection using multi-scale context-dependent deep autoencoding gaussian mixture model
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Application No.: US16248955Application Date: 2019-01-16
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Publication No.: US10853937B2Publication Date: 2020-12-01
- Inventor: Alexandru Niculescu-Mizil , Renqiang Min , Eric Cosatto , Farley Lai , Hans Peter Graf , Xavier Fontaine
- Applicant: NEC Laboratories America, Inc.
- Applicant Address: JP
- Assignee: NEC CORPORATION
- Current Assignee: NEC CORPORATION
- Current Assignee Address: JP
- Agent Joseph Kolodka
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06T7/00 ; G08B29/18 ; G06K9/62 ; G06K9/46 ; G06T3/40

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.
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