Quantitative Methods of Edge Detection

Abstract

Most local operators used in edge detection can be modelled by one of two methods: edge enhancement/thresholding and edge fitting. This dissertation presents a quantitative design and performance evaluation of these methods. The design techniques are based on statistical detection theory and deterministic pattern recognition classification procedure. The performance evaluation methods developed include: (a) deterministic measurement of the edge gradient amplitude; (b) comparison of the probabilities of correct and false edge detection; and (c) figure of merit computation. The design techniques developed are used to optimally design a variety of small and large mask edge enhancement/ thresholding operators. A performance comparison is given between these edge detectors. A new edge fitting algorithm is introduced. The new algorithm is derived in the discrete domain, this allows a direct optimization of the operator's performance. The advantages of new algorithm are better performance with real world pictures and less sensitivity to signal-to-noise ratio.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1978
Accession Number
ADA059124

Entities

People

  • Ikram E. Abdou

Organizations

  • University of Southern California

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Change Detection
  • Cross Correlation
  • Detection
  • Detectors
  • Discrete Fourier Transforms
  • Figure Of Merit
  • Gaussian Noise
  • Heuristic Methods
  • Image Processing
  • Information Science
  • Pattern Recognition
  • Probability
  • Probability Density Functions
  • Random Variables
  • Statistical Analysis
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Approximation Theory.
  • Computer Vision.

Technology Areas

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