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Design of Object Segmentation and Feature Extraction Based On Deep Learning for AFM Image Processing and Analysis System.

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Τίτλος: Design of Object Segmentation and Feature Extraction Based On Deep Learning for AFM Image Processing and Analysis System.
Συγγραφείς: Zhang, Minkang1 (AUTHOR) minkangz2025@gmail.com
Πηγή: Procedia Computer Science. 2025, Vol. 262, p982-991. 10p.
Θεματικοί όροι: Object recognition (Computer vision), Atomic force microscopes, Feature extraction, Image analysis, Deep learning, Image segmentation
Περίληψη: In this paper, an intelligent processing system based on deep learning is constructed to improve the accuracy and automation of data analysis based on the key issues of atomic force microscope image analysis. Aiming at the problem that the traditional scanning method is limited by imaging speed, this paper optimizes the scanning trajectory planning to improve the imaging speed and reduce the influence of deformation by enhancing the continuity and stability of the path. Aiming at the height deviation and structure distortion caused by environmental interference, a robust correction mechanism is proposed, which combines signal optimization and mathematical segvbrfvgsr combination method to effectively compensate image distortion and improve the reliability of morphometric measurement. In terms of target region extraction, a deep learning model integrating multi-level information is constructed to strengthen feature expression and boundary recognition, improve segmentation accuracy and ensure the accuracy of morphological analysis. In this paper, an AFM image set containing multiple types of labeled data is constructed at the same time. The experimental results verify the superiority of the system in object detection and parameter calculation, which provides an intelligent solution for micro-scale morphological analysis and lays a technical foundation for high-precision data processing. [ABSTRACT FROM AUTHOR]
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  – Url: https://www.doi.org/10.1016/j.procs.2025.05.133?
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Items – Name: Title
  Label: Title
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  Data: Design of Object Segmentation and Feature Extraction Based On Deep Learning for AFM Image Processing and Analysis System.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Minkang%22">Zhang, Minkang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> minkangz2025@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Procedia+Computer+Science%22">Procedia Computer Science</searchLink>. 2025, Vol. 262, p982-991. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Atomic+force+microscopes%22">Atomic force microscopes</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, an intelligent processing system based on deep learning is constructed to improve the accuracy and automation of data analysis based on the key issues of atomic force microscope image analysis. Aiming at the problem that the traditional scanning method is limited by imaging speed, this paper optimizes the scanning trajectory planning to improve the imaging speed and reduce the influence of deformation by enhancing the continuity and stability of the path. Aiming at the height deviation and structure distortion caused by environmental interference, a robust correction mechanism is proposed, which combines signal optimization and mathematical segvbrfvgsr combination method to effectively compensate image distortion and improve the reliability of morphometric measurement. In terms of target region extraction, a deep learning model integrating multi-level information is constructed to strengthen feature expression and boundary recognition, improve segmentation accuracy and ensure the accuracy of morphological analysis. In this paper, an AFM image set containing multiple types of labeled data is constructed at the same time. The experimental results verify the superiority of the system in object detection and parameter calculation, which provides an intelligent solution for micro-scale morphological analysis and lays a technical foundation for high-precision data processing. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.1016/j.procs.2025.05.133
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 982
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      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Atomic force microscopes
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Image analysis
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Image segmentation
        Type: general
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      – TitleFull: Design of Object Segmentation and Feature Extraction Based On Deep Learning for AFM Image Processing and Analysis System.
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          Dates:
            – D: 01
              M: 07
              Text: 2025
              Type: published
              Y: 2025
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              Value: 262
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            – TitleFull: Procedia Computer Science
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