MULTI-ENVIRONMENT ATR BASED ON BIDIRECTIONAL PERCEPTION ARCHITECTURES

Abstract

Abstract The U.S. Navy has spent considerable resources researching and developing automatic target recognition (ATR) systems to detect and classify underwater sea mines in sonar imagery. In this proposal we plan to couple a self-organizing bidirectional architecture recently developed for object recognition in video with an active learning system, currently being developed in this program. The active learning module will be hierarchically connected to a content addressable memory and grammatical inference for sonar scene understanding to provide top down context from previous environments and configure appropriately the frontend system parameters to optimize ATR performance for the current uncertain environment. The proposed work will concentrate on the active learning component and its interface with the video perception architecture, to create a cognitive architecture for ATR. We will still include the human in the loop to allow training of global parameters with human supervision. We will test the overall system in the realistic ATR sonar data sets provided by NSWC PC.

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 12, 2016
Source ID
N000141512103

Entities

People

  • José Príncipe

Organizations

  • Office of Naval Research
  • United States Navy
  • University of Florida

Tags

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks