Invention Grant
- Patent Title: Distributed and self-validating computer vision for dense object detection in digital images
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Application No.: US16502417Application Date: 2019-07-03
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Publication No.: US10970543B2Publication Date: 2021-04-06
- Inventor: Gilles Brouard , Lewen Zhao , Natacha Fort , Ke Zeng , Adrien Jacquot , Vitalie Schiopu , Florin Cremenescu
- Applicant: Accenture Global Solutions Limited
- Applicant Address: IE Dublin
- Assignee: Accenture Global Solutions Limited
- Current Assignee: Accenture Global Solutions Limited
- Current Assignee Address: IE Dublin
- Agency: Brinks Gilson & Lione
- Priority: EP19305110 20190129
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06T7/11 ; G06K9/46 ; G06K9/62 ; G06N3/04

Abstract:
A system for object recognition and segmentation from digital images provides an intelligent object recognition and segmentation using one or more multilayer convolutional neural network (CNN) models trained in multiple-stages and in a parallel and distributed manner to improve training speed and efficiency. The training dataset used in each of the multiple training stages for the CNN models are generated, expanded, self-validated from a preceding stage. The trained final CNN models are augmented with post-model filters to enhance prediction accuracy by removing false positive object recognition and segmentation. The system provides improved accuracy to predict object labels to append to unlabeled image blocks in digital images. In one embodiment, the system may be useful for enhancing a digital landmark registry by appending identifying labels on new infrastructure improvements recognized in aerial or satellite land images.
Public/Granted literature
- US20200242357A1 DISTRIBUTED AND SELF-VALIDATING COMPUTER VISION FOR DENSE OBJECT DETECTION IN DIGITAL IMAGES Public/Granted day:2020-07-30
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