Low Level Segmentation of Noisy Imagery

Abstract

A possible approach to image segmentation is first to perform a low- level segmentation. This then allows an original image to be described in terms of a set of simple regions or primitives. Objects in the image may be subsequently recognized by matching these primitives to patterns of primitives in a data base. It is found that current techniques for low-level image segmentation fail when applied to high noise images. An algorithm is presented which overcomes the problems associated with high noise and succeeds in generating low-level segmentations of noisy imagery. The algorithm is shown also to work on low noise data.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1986
Accession Number
ADA169758

Entities

People

  • R. G. White

Organizations

  • Royal Signals and Radar Establishment

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Additives (Chemicals)
  • Algorithms
  • Boundaries
  • Change Detection
  • Computer Vision
  • Contrast
  • Data Reduction
  • Databases
  • Detection
  • Detectors
  • Image Segmentation
  • Information Science
  • Low Noise
  • Pattern Recognition
  • Recognition
  • Statistics

Fields of Study

  • Physics

Readers

  • Computer Vision.