Detection of Step Edges in Noisy One-Dimensional Data.

Abstract

A method of detecting step edges in noisy one-dimensional input data is described. The method involves examination of differences in average gray level over ranges of positions and sizes. Unlike previously described methods, it remains reliable when edges occur close to one another.

Document Details

Document Type
Technical Report
Publication Date
May 01, 1974
Accession Number
ADA005694

Entities

People

  • Azriel Rosenfeld
  • Larry S. Davis

Organizations

  • University of Maryland

Tags

Fields of Study

  • Physics

Readers

  • Approximation Theory.
  • Systems Analysis and Design
  • Vision Science/Vision Psychology/Cognitive Neuroscience.