Advanced Likelihood Generators for Boundary Detection.

Abstract

Previously discussed were the advantages of feature detectors that return likelihoods and some preliminary results with a boundary point detector that returns likelihoods. Now I have developed boundary point detectors that have these properties: Return probabilities; Can be combined robustly; Potential sub-pixel precision; Work with correlated noise; and Can handle multiple gray levels (tested with 255). These detectors were applied both to aerial images and to test images whose boundaries are known. They are compared to established edge detectors. Keywords: Edge detection; Template; Likelihood; Bayesian reasoning.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1987
Accession Number
ADA179876

Entities

People

  • David Sher

Organizations

  • University of Rochester

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Boundaries
  • Change Detection
  • Computer Science
  • Computer Vision
  • Detection
  • Detectors
  • Information Processing
  • Inverse Problems
  • Mathematics
  • New York
  • Pattern Recognition
  • Probability
  • Probability Distributions
  • Random Variables

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Linguistics
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference