An Optimal Scale for Edge Detection

Abstract

Many problems in early vision are ill posed. Edge detection is a typical example. This paper applies regularization techniques to the problem of edge detection. The authors derive an optimal filter for edge detection with a size controlled by the regularization parameter lambda and compare it to the Gaussian filter. A formula relating the signal-to-noise ratio to the parameter lambda is derived from regularization analysis for the case of small values of lambda. Also discussed is the method of Generalized Cross Validation for obtaining the optimal filter scale. Finally, the authors use their framework to explain two perceptual phenomena: coarsely quantized images becoming recognizable by either blurring or adding noise. Keywords: Numerical differentiation.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1988
Accession Number
ADA202747

Entities

People

  • Davi Geiger
  • Tomaso Poggio

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bandwidth
  • Change Detection
  • Crossings
  • Delta Functions
  • Detection
  • Detectors
  • Equations
  • Filters
  • Frequency
  • Frequency Bands
  • Frequency Domain
  • Military Research
  • Recognition
  • Standards
  • Two Dimensional
  • White Noise

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Electromagnetic Wave Scattering and Antenna Radiation Engineering
  • Vision Science/Vision Psychology/Cognitive Neuroscience.