Hybrid Correlation Algorithms. A Bridge Between Feature Matching and Image Correlation,

Abstract

The map matching problem has been in search of an 'optimal universal' matching algorithm since its inception. Because of difficulty in (1) defining a performance criteria for both accuracy and probability of correct match, and (2) in knowing a priori the distributions associated with all map errors, most researchers have resorted to the use of 'ad hoc' algorithms. These have generally been divided into two classes--feature matching and correlation. Up to the present time there have been two basic classes of map matching algorithms--those based on feature matching techniques and those based on image correlation. This paper describes a new class of hybrid correlation algorithms which incorporate features as an integral part of the matching process. These algorithms can be implemented such that it is not necessary to extract features from the sensed image. This paper concludes by showing the domains in which each class of matching algorithm (feature matching, image correlation, and hybrid algorithm) is most appropriate.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1979
Accession Number
ADA095436

Entities

People

  • Joseph A. Ratkovic

Organizations

  • RAND Corporation

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Acquisition
  • Algorithms
  • Altitude
  • Autocorrelation
  • Boundaries
  • Computer Vision
  • Data Science
  • Displacement
  • Distortion
  • Errors
  • Feature Extraction
  • Information Science
  • Intensity
  • Pattern Recognition
  • Preprocessing
  • Simulations

Fields of Study

  • Computer science

Readers

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