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Difference from Background: Limit of Detection

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Related Experiment Video

Updated: Jun 24, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Efficient edge detection using simplified gabor wavelets.

Wei Jiang1, Kin-Man Lam, Ting-Zhi Shen

  • 1Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|April 2, 2009
PubMed
Summary
This summary is machine-generated.

Simplified Gabor wavelets (SGWs) offer efficient feature extraction for edge detection. Our SGW algorithm matches Gabor wavelet performance with faster runtimes, outperforming conventional methods in accuracy and complexity.

Related Experiment Videos

Last Updated: Jun 24, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Gabor wavelets (GWs) are effective for local feature extraction in recognition and edge detection.
  • The computational intensity of GWs limits their real-time application.

Purpose of the Study:

  • To propose simplified Gabor wavelets (SGWs) and an efficient algorithm for feature extraction.
  • To improve the practicality of Gabor-based methods for real-time edge detection.

Main Methods:

  • Development of a simplified Gabor wavelet (SGW) set.
  • Implementation of an efficient feature extraction algorithm using SGWs.
  • Utilizing the fast Fourier transform for accelerated computation.

Main Results:

  • SGW-based edge detection achieves performance comparable to traditional GWs.
  • SGW feature extraction demonstrates significantly faster runtimes than GWs.
  • The proposed method surpasses Canny and other conventional edge detectors in accuracy and computational efficiency.

Conclusions:

  • SGWs provide an efficient alternative to GWs for feature extraction in edge detection.
  • The proposed algorithm enhances real-time applicability without compromising performance.
  • This method offers superior accuracy and computational advantages over existing techniques.