Optimal Likelihood Generators for Edge Detection under Gaussian Additive Noise.

Abstract

A technique is presented for determining the probability of an edge at a point in an image that is convolve with a linear blurring function and also with uncorrelated Gaussian additive noise. The ideal image is modeled by a set of templates for local neighborhoods. Every neighborhood in the ideal image is assumed to fit one of the templates with height probability. A computationally feasible scheme to compute the probability of edges is given. The output of several of the likelihood generators based on this model can be combined to form a more robust likelihood generator. Keywords: Edge detection; Template; Likelihood; Bayesian reasoning.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1986
Accession Number
ADA179945

Entities

People

  • David Sher

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Additives (Chemicals)
  • Algorithms
  • Artificial Intelligence
  • Boundaries
  • Change Detection
  • Classification
  • Computer Graphics
  • Computer Science
  • Computer Vision
  • Detection
  • Detectors
  • Generators
  • New York
  • Probability
  • Security
  • Template Patterns

Fields of Study

  • Mathematics

Readers

  • Electromagnetic Wave Scattering and Antenna Radiation Engineering
  • Neural Network Machine Learning.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms