ICA Feature Extraction and SVM Classification of FLIR Imagery

Abstract

Detection and clutter-rejection algorithms are being developed to process forward-looking infrared (FLIR) imagery. Feature selection is an important issue, with principal components analysis (PCA) and independent components analysis (ICA) constituting two algorithms of interest. With regard to processing the features, we are examining Bayesian learning-machine algorithms, such as the relevance-vector machine. All algorithms are applied to measured data.

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Document Details

Document Type
Technical Report
Publication Date
Sep 15, 2005
Accession Number
ADA441506

Entities

People

  • Lawrence Carin

Organizations

  • Duke University

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computer Vision
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Feature Extraction
  • Feature Selection
  • Hidden Markov Models
  • Learning Machines
  • Machine Learning
  • Markov Models
  • Probability
  • Signal Processing
  • Supervised Machine Learning
  • Target Classification
  • Target Recognition

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks