Bayesian Automated Target Recognition: Models and Algorithms

Abstract

The primary goal of this research was to develop representations, models, and algorithms for use in Bayesian automated recognition of objects from their images. Despite focused efforts in the area of image understanding in recent years, a fresh look was needed to highlight the progress and the limitations. Our research was focused along the following three broad themes: (i) development of efficient representations of the objects of interest (or their images) using nonlinear manifolds, (ii) development of parametric probability models for capturing object and clutter variability, and (iii) development of algorithms for solving inference problems on nonlinear manifolds that arise in object recognition.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 2003
Accession Number
ADA428831

Entities

People

  • Anuj Srivastava
  • Xiuwen Liu

Organizations

  • Florida State University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Automated Target Recognition
  • Bayesian Networks
  • Computer Science
  • Computer Vision
  • Databases
  • Dimensionality Reduction
  • Image Processing
  • Information Science
  • Object Recognition
  • Probability
  • Recognition
  • Signal Processing
  • Statistical Analysis
  • Statistical Inference
  • Target Recognition

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms