Biomorphic Networks for ATR and Higher-Level Processing.

Abstract

Invariant features in two dimensional binary images are extracted in a single layer network of locally coupled spiking (pulsating) model neurons with prescribed synapto dendritic response. The feature vector for an image is represented as invariant structure in the aggregate histogram of interspike intervals obtained by computing time intervals between successive spikes produced from each neuron over a given period of time and combining such intervals from all neurons in the network into a histogram. Simulation results show that the feature vectors are more pattern specific and invariant under translation, rotation, and change in scale or intensity than achieved in earlier work. We also describe an application of such networks to segmentation of line (edge enhanced or silhouette) images. The biomorphic spiking network's capabilities in segmentation and invariant feature extraction may prove to be, when they are combined, valuable in Automated Target Recognition (ATR) and other automated object recognition systems.

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Document Details

Document Type
Technical Report
Publication Date
Jan 10, 1998
Accession Number
ADA347722

Entities

People

  • Nabil H. Farhat

Organizations

  • Moore School of Electrical Engineering

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Character Recognition
  • Classification
  • Coding
  • Computer Vision
  • Electrical Engineering
  • Feature Extraction
  • Firing Rate
  • Gray Scale
  • Membrane Potentials
  • Nervous System
  • Neural Networks
  • Pattern Recognition
  • Pulse Generators
  • Signal Processing
  • Target Recognition
  • Translations

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference