Automatic Feature Extraction/Algorithm Testing.

Abstract

Feature extraction results using an Improved Texture Clustering Algorithm were compared to photo interpreters' manual segmentation/classification results. The clustering algorithm segments digital images via texture and edge attributes. The probability of detecting a target or feature is increased if the number of segments is reduced; therefore, the algorithm merges segments via edge thresholds. Evaluation showed general agreement, but the edge attributes would apply more to target detection than to feature classification. Keywords: Digital image processing. (Author)

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1985
Accession Number
ADA164325

Entities

People

  • Ned A. Ferris

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Clustering
  • Computer Vision
  • Detection
  • Digital Image Processing
  • Digital Images
  • Extraction
  • Feature Extraction
  • Image Processing
  • Images
  • Information Processing
  • Target Detection

Readers

  • Computer Vision.

Technology Areas

  • AI & ML