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Electronics
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New Advances in Optical Imaging and Metrology
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New Advances in Optical Imaging and Metrology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Optoelectronics".

Deadline for manuscript submissions: closed (16 December 2024) | Viewed by 2470

Special Issue Editors

Dr. Zhoujie Wu

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
Interests: optical metrology; 3d shape measurement; 3D deformation measurement
Dr. Junfei Shen

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
Interests: computational imaging; deep learning

Special Issue Information

Dear Colleagues,

Optical imaging and metrology are rapidly developing fields with numerous applications in various domains, including medicine, biology, engineering, and physics. Recently, this field has been making great progress with prevalence of computational optical imaging and metrology. The development tendency of computational imaging and metrology is towards higher resolution and accuracy, faster processing speed, and more sophisticated algorithms for data analysis and interpretation. One key of this trend is the increasing demand for accurate and efficient measurement and imaging techniques for a wide range of applications. Another important trend is the integration of optics with other technologies, such as artificial intelligence (AI), to enable more advanced data processing and analysis. All these developments enable new applications and capabilities that were previously not possible, and will continue to drive innovation in this exciting field.

This special issue aims to highlight recent advances in the development and application of optical imaging and metrology techniques, with particular emphasis on novel approaches, breakthroughs, and emerging applications.

The topics of interest for this special issue include, but are not limited to:

  • Three-dimensional shape and deformation measurement.
  • Strain analysis.
  • Novel approaches in optical metrology, including fringe projection profilometry, digital image correlation and phase measuring deflectometry.
  • Emerging applications of optical techniques in industry, manufacturing, and quality control.
  • Computational multispectral imaging
  • Novel approaches in computer vision, computaional imaging, deep-learning-based image processing

Dr. Zhoujie Wu
Dr. Junfei Shen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optical metrology
  • 3D shape measurement
  • 3D deformation measurement
  • strain snslysis
  • computaional imgaing
  • multispectral imaging

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Published Papers (2 papers)

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Research

11 pages, 1876 KiB  
Article
A Fast Evaluation Method for Spatial Point Measurement Accuracy in a Large-Scale Measurement System
by Yusong Liu, Wenbo Guo, Yuanyuan Pang and Bo Zheng
Electronics 2024, 13(13), 2428; https://doi.org/10.3390/electronics13132428 - 21 Jun 2024
Viewed by 870
Abstract
In the application domain of large-scale high-precision measurement systems, accurately calibrating the precision of point position measurements is a pivotal issue. Traditional calibration methods rely on laser interferometers and high-precision displacement stages, which are not only costly but also challenging to implement in [...] Read more.
In the application domain of large-scale high-precision measurement systems, accurately calibrating the precision of point position measurements is a pivotal issue. Traditional calibration methods rely on laser interferometers and high-precision displacement stages, which are not only costly but also challenging to implement in fixed measurement systems. Addressing this challenge, this study introduces an evaluation method for the spatial point measurement accuracy in large-scale fixed high-precision measurement systems. The models for the relationship between the limit deviation and the maximum deviation of finite measurements were established, as well as the limit deviation and point position measurement accuracy. The spatial point position accuracy of the measurement field was calculated by the measurement errors of a calibration rod. The algorithm was validated using a large-scale measurement platform based on photogrammetric technology. Experimental results demonstrate that the method achieved a point position measurement accuracy calibration better than 0.1 mm within a 20 m measurement range, effectively enhancing the measurement data’s accuracy and reliability. This research optimizes the calibration process for large-scale fixed measurement systems, improves calibration efficiency, and obviates the need for complex equipment to complete the calibration process, which is of considerable importance to the development of high-precision spatial point position measurement technology. Full article
(This article belongs to the Special Issue New Advances in Optical Imaging and Metrology)
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9 pages, 3926 KiB  
Article
Developing a Prototype Device for Assessing Meat Quality Using Autofluorescence Imaging and Machine Learning Techniques
by Eric Zhou, Saabah B. Mahbub, Ewa M. Goldys and Sandhya Clement
Electronics 2024, 13(9), 1623; https://doi.org/10.3390/electronics13091623 - 24 Apr 2024
Viewed by 1070
Abstract
Meat quality determination is now more vital than ever, with an ever-increasing demand for meat, especially with a greater desire for high-quality beef. Many existing qualitative methods currently used for meat quality assessment are strenuous, time-consuming, and subjective. The quantitative techniques employed are [...] Read more.
Meat quality determination is now more vital than ever, with an ever-increasing demand for meat, especially with a greater desire for high-quality beef. Many existing qualitative methods currently used for meat quality assessment are strenuous, time-consuming, and subjective. The quantitative techniques employed are time-consuming, destructive, and expensive. In the search for a quantitative, rapid, and non-destructive method of determining meat quality, the use of autofluorescence has been employed and has demonstrated its capabilities to characterise meat grades by identifying biochemical features such as the intramuscular fat and tryptophan content through the excitation of meat samples and the collection and analysis of the emission data. Despite its success, the method remains expensive and inaccessible, thus preventing it from being translated into small-scale industry applications. This study will detail the process taken to design and construct a low-cost, miniature prototype device that could successfully distinguish between varying meat grades using autofluorescence imaging and machine learning techniques. Full article
(This article belongs to the Special Issue New Advances in Optical Imaging and Metrology)
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