Fast Object Recognition in Noisy Images Using Simulated Annealing.

Abstract

A fast simulated annealing algorithm is developed for automatic object recognition. The normalized correlation coefficient is used as a measure of the match between a hypothesized object and an image. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, for example, traffic signs, can be recognized by an autonomous vehicle or a navigating robot. The algorithm works well in noisy, real-world images of complicated scenes for model images with high information content.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1994
Accession Number
ADA295771

Entities

People

  • Margrit Betke
  • Nicholas C. Makris

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Biomedical

DTIC Thesaurus Topics

  • Adaptive Systems
  • Algorithms
  • Annealing
  • Artificial Intelligence
  • Autonomous Vehicles
  • Coefficients
  • Cognitive Science
  • Computer Vision
  • Gaussian Noise
  • Image Processing
  • Image Recognition
  • Object Recognition
  • Pattern Recognition
  • Recognition
  • Signal Processing
  • Template Patterns
  • Two Dimensional

Fields of Study

  • Computer science

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy