Variational Object Recognition and Grouping Network

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047618C0055
Agency Tracking Number: NGA-P1-18-03
Amount: $99,998.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NGA181-005
Solicitation Number: 2018.1
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-07
Award End Date (Contract End Date): 2019-06-15
Small Business Information
1845 West 205th Street, Torrance, CA, 90501
DUNS: 080921977
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Yong Wu
 (310) 320-1827
 ars@intellisenseinc.com
Business Contact
 Laura Miller
Phone: (310) 320-1827
Email: notify@intellisenseinc.com
Research Institution
N/A
Abstract
To address the National Geospatial-Intelligence Agency (NGA) need for overhead imagery analysis algorithms that provide uncertaintymeasures for object recognition and aggregation, Intellisense Systems, Inc. (ISS) proposes to develop a new Variational Object Recognition andGrouping Network (VORGNet) system. It is based on the innovation of implementing a Bayesian convolutional neural network (CNN) thatdetects objects in overhead images with uncertainty measures, followed by object grouping and group classification, taking into accountvarious uncertainties. The object detection uncertainty is estimated by variational inference, conveniently integrated with the dropoutregularization technique widely used in deep learning. The detected objects are grouped by fuzzy clustering. Each group is characterized by itsmembers and the spatial configuration and classified by supervised machine learning and domain knowledge. Monte Carlo (MC) simulationsincorporate the object detection uncertainty and the group membership uncertainty to quantify the overall uncertainty in aggregation. InPhase I, ISS will prove VORGNets feasibility by demonstrating its ability to obtain uncertainty measures for an object detection CNN andperform uncertainty-aware object aggregation on publicly available overhead imagery datasets. In Phase II, ISS will refine VORGNetsperformance on various overhead imagery datasets and develop test metrics jointly with the government for performance evaluation.

* Information listed above is at the time of submission. *

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