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Local scale control for edge detection and blur estimation

  • James H. Elder
  • Steven W. Zucker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1065)

Abstract

Selecting the appropriate spatial scale for local edge analysis is a challenge for natural images, where blur scale and contrast may vary over a broad range. While previous methods for scale adaptation have required the global solution of a non-convex optimization problem [8], it is shown that knowledge of sensor properties and operator norms can be exploited to define a unique, locally-computable minimum reliable scale for local estimation. The resulting method for local scale control allows edges spanning a broad range of blur scales and contrasts to be reliably localized by a single system with no input parameters other than the second moment of the sensor noise. Local scale control further permits the reliable estimation of local blur scale in complex images where the conditions demanded by Fourier methods for blur estimation break down.

Keywords

Pattern Anal Sensor Noise Gradient Magnitude Edge Model Shadow Boundary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • James H. Elder
    • 1
  • Steven W. Zucker
    • 2
  1. 1.NEC Research InstitutePrincetonUSA
  2. 2.Centre for Intelligent MachinesMcGill UniversityMontréalCanada

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