Study of Features of Binary Images Using Algebra Techniques

Abstract

The aim of this project was to investigate features of binary images by considering a special case of the Image Algebra methodology obtained by representing digitized images (both monochrome and colored) by certain polynomials in two variables with coefficients from the binary field. Since polynomials can be easily manipulated and our proposed operators can be described conveniently in terms of algebraic operations on these polynomials, this approach provides a significant foundation for practical applications which would be of significant interest to AFOSR. Our specific objectives have been as follows. We have developed algebraic operators in the context of the polynomial approach to determine the contour, magnification and shrinking, and a sequence of approximations (from finer to coarser) of a binary image. Further, we have extended our techniques to process gray images and do operations such as template decomposition, shape decomposition, connected component labelling. Also, we have developed an algebraic system to process colored images. Also, we have developed some fast sequential and parallel thinning algorithms. We have also extended the polynomial approach to three dimensional by developing equivalents of the standard morphological operations and applying these to do a number of operations for the understanding of three dimensional objects.

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Document Details

Document Type
Technical Report
Publication Date
Apr 04, 1991
Accession Number
ADA242010

Entities

People

  • Prabir Bhattacharya

Organizations

  • University of Nebraska-Lincoln Department of Computer Science and Engineering

Tags

Communities of Interest

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

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computations
  • Computer Science
  • Computer Vision
  • Digital Images
  • Engineering
  • Image Processing
  • Image Recognition
  • Object Recognition
  • Parallel Computing
  • Parallel Processing
  • Pattern Recognition
  • Recognition
  • Software Development
  • Standards
  • Three Dimensional

Readers

  • Computational Fluid Dynamics (CFD)
  • Image Processing and Computer Vision.
  • Linear Algebra