Structural Analysis of Natural Textures

Abstract

In this dissertation a general method is presented for the structural analysis of natural texture images. Edge Repetition Arrays (ERAs) are calculated for the edge and direction images corresponding to the natural texture image being analyzed. Each ERA entry is the normalized frequency of occurrence of specific types of edge matches occurring with a particular angle and distance separation. The two types of edge matches sought are for element size and spacing. Hence, a one-dimensional structural texture profile can be inferred from the information inherent in these arrays. An algorithm designed to automatically interpret ERA results is presented. This algorithm produces a one- dimensional structural description of the texture, and provides a starting point for the two-dimensional texture primitive search. A texture primitive extraction algorithms is also developed. It uses the information inherent in the above- mentioned texture description to identify the locations of various type of elements within the texture image. The results produced are in the form of individual and composite texture primitive masks as well as descriptions for the individual texture primitive types. A structural texture description should include information about the manner in which the primitives are arranged within the texture. Since the texture primitive masks pinpoint the texture element locations they can be used to calculate a set of rules which characterize this arrangement.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1981
Accession Number
ADA110032

Entities

People

  • Felicia M. D'a Vilnrotter

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Capillary Electrophoresis
  • Computer Vision
  • Computers
  • Data Science
  • Feature Extraction
  • Frequency
  • Image Processing
  • Information Science
  • Pattern Recognition
  • Plastic Explosives
  • Probability
  • Random Variables
  • Recognition
  • Statistical Analysis
  • Structural Analysis
  • Two Dimensional

Readers

  • Computer Vision.

Technology Areas

  • AI & ML
  • Space