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Resolution limit of image analysis algorithms

Author

Listed:
  • Edward A. K. Cohen

    (Imperial College London)

  • Anish V. Abraham

    (Texas A&M University
    Texas A&M University)

  • Sreevidhya Ramakrishnan

    (Texas A&M University
    Texas A&M University)

  • Raimund J. Ober

    (Texas A&M University
    Texas A&M University
    University of Southampton)

Abstract

The resolution of an imaging system is a key property that, despite many advances in optical imaging methods, remains difficult to define and apply. Rayleigh’s and Abbe’s resolution criteria were developed for observations with the human eye. However, modern imaging data is typically acquired on highly sensitive cameras and often requires complex image processing algorithms to analyze. Currently, no approaches are available for evaluating the resolving capability of such image processing algorithms that are now central to the analysis of imaging data, particularly location-based imaging data. Using methods of spatial statistics, we develop a novel algorithmic resolution limit to evaluate the resolving capabilities of location-based image processing algorithms. We show how insufficient algorithmic resolution can impact the outcome of location-based image analysis and present an approach to account for algorithmic resolution in the analysis of spatial location patterns.

Suggested Citation

  • Edward A. K. Cohen & Anish V. Abraham & Sreevidhya Ramakrishnan & Raimund J. Ober, 2019. "Resolution limit of image analysis algorithms," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-08689-x
    DOI: 10.1038/s41467-019-08689-x
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    Cited by:

    1. Qionglin Liu & Yinghui Tang & Miaomiao Yu, 2025. "A new discrete-time queueing model to optimize cargo dispatch for a warehouse," Operational Research, Springer, vol. 25(1), pages 1-33, March.

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