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Related Concept Videos

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Aliasing

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Bandpass Sampling01:17

Bandpass Sampling

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Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Upsampling01:22

Upsampling

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Optimal edge detector design I: parameter selection and noise effects.

IEEE transactions on pattern analysis and machine intelligence·2011
Same author

Optimal Edge Detector Design II: Coefficient Quantization.

IEEE transactions on pattern analysis and machine intelligence·2011
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The asymptotic optimal frequency domain filter for edge detection.

W H Lunscher1

  • 1Departrment of Electrical Engineering, University of British Columbia, Vancouver, B.C., Canada V6T 1Z2.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary

This study corrects a dimensional error in an edge detection filter, improving its accuracy for image analysis. The new filter maximizes energy within a specified interval, outperforming previous methods.

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Area of Science:

  • Image processing and computer vision
  • Signal processing

Background:

  • An earlier edge detection filter maximized energy within a specified interval.
  • The initial filter derivation involved a prolate spheroidal wave function.

Purpose of the Study:

  • To correct a dimensional error in the asymptotic approximation of an edge detection filter.
  • To present the corrected derivation of the asymptotic optimal filter.

Main Methods:

  • Analysis of asymptotic approximation of prolate spheroidal wave functions.
  • Derivation of a corrected edge detection filter.

Main Results:

  • A dimensional error in the previous filter derivation was identified and corrected.
  • The corrected filter was compared to the Marr-Hildreth filter for verification.

Conclusions:

  • The corrected derivation provides a more accurate asymptotic optimal edge detection filter.
  • The new filter demonstrates improved performance in edge feature detection.