Design of Edge Detectors for Reduced Images.

Abstract

The development of algorithms to extract informational features from imagery is an area of active research. These algorithms enable computerized devices to automatically locate and identify objects in the field of view of a sensor. An important Air Force application is automatic target identification and weapon guidance. Edges in an image contain much of the information necessary to classify objects. This investigation has centered on finding methods for reducing an image so as to maximize the retention of edge information which was subsequently extracted. The Hotelling transform which reduces image data so as to minimize intensity mean-square error (IMSE) in the reconstructed image was also found to have significantly better edge retaining ability than simple averaging. The reconstructed edges were quantitatively compared to those in the original images using MSE and receiver operating characteristic based measures. One such measure used was the gradient mean-square error (GMSE). Both the reconstructed IMSE and GMSE using the Hotelling transform tend to decrease as the encoding block size increases. An equation relating GMSE to IMSE was developed. For image gradient blocks that are independently reconstructed, the linear transformation matrix A that minimizes the reconstructed GMSE and in that sense maximizes edge retention was derived. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1984
Accession Number
ADA145700

Entities

People

  • D. J. Healy

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Change Detection
  • Classification
  • Coding
  • Data Compression
  • Data Reduction
  • Detection
  • Detectors
  • Eigenvectors
  • Electrical Engineering
  • Engineering
  • Equations
  • False Alarms
  • Intensity
  • Notation
  • Warning Systems

Readers

  • Computer Vision.
  • Critical Infrastructure Protection in CBRN and WMD Threats.
  • Statistical inference.