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Related Experiment Video

Updated: Jul 16, 2026

Picometer-Precision Atomic Position Tracking through Electron Microscopy
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Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Adaptive Edge-Response-Based Subpixel Localization Method for Microscopic Vision-Based Alignment Measurement.

Xuefeng Sun1,2, Weibo Wang1,2,3

  • 1Ultra-Precision Optoelectronic Instrument Engineering, Harbin Institute of Technology, Harbin 150080, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

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This study introduces an adaptive edge-response model for precise microscopic alignment. The new method significantly enhances subpixel edge localization accuracy, improving repeatability by 52% in challenging imaging conditions.

Area of Science:

  • Optics and Photonics
  • Metrology
  • Microscopy

Background:

  • Microscopic vision-based alignment is crucial for micro-/nanoscale positioning.
  • Subpixel edge-center estimation accuracy is key for measurement repeatability.
  • Defocus and contamination degrade image quality, hindering conventional alignment methods.

Purpose of the Study:

  • To develop an adaptive edge-response modeling method for robust subpixel edge localization.
  • To overcome limitations of conventional methods in degraded microscopic imaging.
  • To improve the accuracy and repeatability of microscopic alignment measurements.

Main Methods:

  • Proposed an adaptive edge-response modeling method.
  • Constructed an amplitude function combining gradient peak and slope for adaptive response.
Keywords:
edge-response modelingmicroscopic vision-based alignmentoptical measurementsubpixel localization

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Last Updated: Jul 16, 2026

Picometer-Precision Atomic Position Tracking through Electron Microscopy
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Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

  • Developed a unified model for multi-edge hybrid bonding marks, overcoming Sigmoid model limitations.

    • Main Results:

    • The adaptive model effectively suppresses interference from edge broadening and pseudo-gradient peaks.
    • Improved subpixel fitting and localization accuracy under degraded imaging conditions.
    • Achieved approximately 52% improvement in subpixel edge localization repeatability compared to conventional methods.

    Conclusions:

    • The proposed adaptive edge-response modeling method enhances microscopic vision-based alignment accuracy.
    • The method meets high-precision alignment requirements, even with image quality degradation.
    • Offers a more robust solution for subpixel edge localization in practical micro-/nanoscale applications.