You are here

Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047618C0065
Agency Tracking Number: NGA-P1-18A-01
Amount: $100,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: 1
Solicitation Number: 2018.0
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-10
Award End Date (Contract End Date): 2019-06-15
Small Business Information
6800 Cortona Drive
Goleta, CA 93117
United States
DUNS: 054672662
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Christopher Agh
 (805) 968-6787
Business Contact
 Marcella Lindbery
Phone: (805) 968-6787
Research Institution
 The Pennsylvania State University
 Helen Tyson
110 Technology Center Building
University Park, PA 16802
United States

 (814) 865-1372
 Domestic Nonprofit Research Organization

On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when limited labeled examples but potentially many unlabeled data exist. Furthermore, the classifier will be able to discover new object classes and target signatures not found in the training data but are well-suited for IR data exploitation. We will also develop algorithms for the generation of infrared images of a given class of interest from one modality (for which available data resources may be scarce) from images from another modality (for which available data resources may be plentiful). This method exploits the paradigm of deterministic annealing to learn associations between pairs of images from the two modalities available image databases.

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

US Flag An Official Website of the United States Government