Design Gabor Filters for Texture Segmentation

Abstract

Many texture-segmentation schemes use an elaborate bank of filters to decompose a textured image -into a joint space/spatial-frequency representation. While these schemes show promise and some analytical work has been done, the relationship between texture differences and the filter configurations required to discriminate them remains largely known. This thesis examines the issue of designing individual filters. Analysis based on mathematically defined texture models shows that applying a properly configured bandpass filter to a textured image produces distinct output discontinuities at texture boundaries. Depending on the type of texture difference and the filter parameters, these discontinuities form one of four characteristic signatures: a step, valley, ridge, or a step change in average local output variation. Accompanying experimental evidence indicates that these signatures are useful for segmenting an image. Initially, a simple 1-D texture model is used to derive the step and valley signatures. Ibis model leads to a simple analytical development providing helpful insight. The 1-D model, however, has certain limitations. For example, the existence of the ridge signature cannot be shown using this model. Consequently, a more general 2-D model is also presented, leading to a more complex but informative analysis.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1992
Accession Number
ADA256326

Entities

People

  • A. Maida
  • D. F. Dunn
  • J. Wakeley
  • William J. Higgins

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bandpass Filters
  • Boundaries
  • Change Detection
  • Computational Science
  • Computer Vision
  • Detection
  • Detectors
  • Filters
  • Frequency
  • Frequency Bands
  • Information Processing
  • Pattern Recognition
  • Random Variables
  • Three Dimensional
  • Two Dimensional

Readers

  • Computer Vision.
  • Theoretical Analysis.

Technology Areas

  • Space