Texture Tone Feature Extraction and Analysis.

Abstract

A new texture measurement and the Mahalanobis classifier with a generalized inverse scheme were developed to generate decision maps of terrain features with digitized B/W photographs on a pixel by pixel basis. Eight scences within the Northeast test area, four low altitude and four high altitude, were analyzed yielding a hit-rate of about 90% with properly digitized image data. To determine the degree of non-normal behavior of the texture variables, the stable distribution models were utilized. Methods of estimating the stable parameters of the texture variables were developed. It is found that fifty % of the texture variables are not normally distributed. Since the stable distribution models are capable of incorporating the skewness parameters into the classification process, it is recommended as a new classifier for image data analysis.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1977
Accession Number
ADA045542

Entities

People

  • Eugene Klimko
  • Shin-yi Hsu

Organizations

  • Binghamton University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Altitude
  • Artificial Intelligence
  • Data Processing
  • Data Science
  • Data Sets
  • Digital Data
  • Elevation
  • High Altitude
  • Image Processing
  • Images
  • Information Processing
  • Information Science
  • Low Altitude
  • Machine Learning
  • New York
  • Photographs
  • Photography

Readers

  • Computer Vision.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference