Genetic Contour Matching: A Feasibility Study.

Abstract

This report presents a genetic algorithm that detects, in grey-level images, instances of a parameterized object contour model. Object detection is formulated as an optimization problem. The objective function measures the evidential support for any particular projection of the parametrized object contour model onto the input image. A genetic algorithm is used to find a set of parameters which provide a good (though not necessarily optimal) interpretation of the image in terms of the model. object classification can be performed through comparison of the parameters of the best fit with those of the prototypes in a data base. The genetic contour matching algorithm can therefore be used as a basis for a scale and pose-invariant object recognition system. The results of some preliminary tests demonstrate the feasibility of the approach. Image processing, Automatic target recognition, Pattern recognition, Edge detection, Recognition, Genetic algorithms, Target acquisition, Image processing.

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1994
Accession Number
ADA285202

Entities

People

  • A. Toet

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Change Detection
  • Classification
  • Computer Vision
  • Databases
  • Detection
  • Feasibility Studies
  • Genetic Algorithms
  • Image Processing
  • Image Recognition
  • Object Recognition
  • Pattern Recognition
  • Recognition
  • Target Acquisition
  • Target Recognition

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.

Technology Areas

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