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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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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

Detection of edges using range information.

A Mitiche1, J K Aggarwal

  • 1Department of Electrical Engineering and the Laboratory for Image and Signal Analysis, University of Texas, Austin, TX 78712; Institut National de la Recherche Scientifique, Verdun.

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

This study presents a novel range edge detection method for 3-D shape analysis using laser range data. The procedure is designed to be robust against noise, improving 3-D object boundary extraction.

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
  • Robotics
  • 3D Sensing

Background:

  • Range data from sensors like lasers are crucial for obtaining 3-D shape information.
  • Extracting object boundaries (jump and surface edges) from range data is essential for scene understanding.
  • Measurement accuracy in laser range finders is limited by signal power, leading to noise sensitivity.

Purpose of the Study:

  • To develop a range edge detection procedure for 3-D shape analysis.
  • To create a method with low sensitivity to noise in range measurements.
  • To effectively utilize available knowledge on range measurement accuracy for improved edge detection.

Main Methods:

  • Utilizing 3-D shape information derived from range data.
  • Implementing an edge detection procedure specifically for range measurements.
  • Designing the procedure to incorporate knowledge of range measurement accuracy to mitigate noise.

Main Results:

  • A range edge detection procedure with reduced sensitivity to noise was developed.
  • The method effectively extracts jump boundaries and surface edges from laser range data.
  • The procedure leverages sensor-specific accuracy information for enhanced performance.

Conclusions:

  • The proposed range edge detection method offers a robust approach for 3-D shape analysis.
  • This technique improves the reliability of extracting object boundaries from noisy laser range data.
  • The method provides a foundation for more accurate 3-D scene interpretation in applications using range sensors.