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Related Experiment Video

Updated: May 29, 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

Geometric constructions for predicting hough transform performance.

S D Shapiro1, A Iannino

  • 1Department of Electrical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces graphical methods to assess Hough transform performance in detecting straight lines within noisy digital images. It reveals how noise, like quantization errors, impacts accuracy, highlighting the significance of endpoints for collinear points.

Related Experiment Videos

Last Updated: May 29, 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
  • Pattern Recognition

Background:

  • The Hough transform is a widely used technique for detecting shapes, particularly straight lines, in digital images.
  • Image noise, such as quantization errors, can significantly degrade the performance and accuracy of the Hough transform.
  • Estimating the performance of the Hough transform under noisy conditions is crucial for reliable line detection.

Purpose of the Study:

  • To develop and present graphical techniques for evaluating Hough transform performance in noisy digital images.
  • To explicitly demonstrate the impact of noise on the accuracy of estimating collinear points.
  • To establish theoretical bounds and guidelines for the accumulator method in Hough transform-based line detection.

Main Methods:

  • Development of graphical methods for performance estimation.
  • Analysis of noise influence, specifically quantization errors, on collinear point estimation.
  • Derivation of exact bounds for sets of collinear points.
  • Formulation of theorems for accumulator grid size determination.

Main Results:

  • Graphical techniques effectively illustrate Hough transform performance under noise.
  • Noise significantly affects the accuracy of estimating collinear points, with endpoints being most critical.
  • Exact bounds confirm the general significance of endpoints for collinear point sets.
  • Theorems provide guidance on optimal accumulator grid sizing for improved accuracy.

Conclusions:

  • The presented graphical techniques offer valuable insights into Hough transform robustness against noise.
  • Endpoint information is paramount for accurate line detection in noisy images.
  • The derived theorems aid in optimizing the Hough transform's accumulator method for practical applications.