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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Multiple-window parallel adaptive boundary finding in computer vision.

D B Cooper1, F P Sung

  • 1MEMBER, IEEE, Division of Engineering, Brown University, Providence, RI 02912.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
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This study introduces parallel multiple-window algorithms for accurate object boundary estimation in noisy images. The method uses dynamic programming within a maximum likelihood framework for robust boundary detection and reconstruction.

Area of Science:

  • Image processing and computer vision
  • Computational imaging
  • Pattern recognition

Background:

  • Accurate object boundary estimation is crucial in various imaging applications, including medical scans and infrared imaging.
  • Noisy image data and complex object shapes present significant challenges to traditional boundary detection methods.
  • Existing algorithms often struggle with high variability and internal object structures.

Purpose of the Study:

  • To develop and evaluate a novel parallel multiple-window algorithm for robust object boundary estimation in noisy images.
  • To integrate boundary finding within a unified maximum likelihood estimation framework.
  • To improve the accuracy and efficiency of boundary detection in diverse imaging scenarios.

Main Methods:

  • Partitioning the image field into an array of rectangular windows for parallel processing.

Related Experiment Videos

Last Updated: May 29, 2026

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
07:45

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

  • Employing dynamic programming-based boundary finders within each window.
  • Seaming boundary segments from individual windows to reconstruct global object boundaries.
  • Utilizing a maximum likelihood estimation framework for overall boundary estimation.

    • Main Results:

    • Demonstrated effectiveness of the parallel multiple-window approach for estimating highly variable object boundaries.
    • Successful integration of dynamic programming with maximum likelihood estimation for precise boundary localization.
    • Validation of the F-test for efficient boundary presence detection within windows.
    • Analysis showing improved boundary recognition probability using coarse pixels with chi-square or F-tests.

    Conclusions:

    • The proposed parallel multiple-window boundary estimation algorithm offers a robust solution for noisy and complex imagery.
    • The maximum likelihood framework provides a principled approach to integrating local boundary findings into global structures.
    • Computational efficiency is enhanced through techniques like the F-test and the use of coarse pixels, improving practical applicability.