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Feature Papers in Sensing and Imaging 2024
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Feature Papers in Sensing and Imaging 2024

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4359

Special Issue Editors

Prof. Dr. Sylvain Girard

E-Mail Website
Guest Editor
Laboratoire Hubert Curien, CNRS UMR 5516, Université de Lyon, 42000 Saint-Étienne, France
Interests: fiber sensors; optical sensors; image sensors; optical materials; radiation effects
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christoph M. Friedrich

E-Mail Website
Guest Editor
1. Department of Computer Science, University of Applied Sciences and Arts Dortmund (FH Dortmund), 44227 Dortmund, Germany
2. Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, 45122 Essen, Germany
Interests: machine learning; computational intelligence; biomedical applications; interpretable machine learning; natural language processing (NLP); computer vision; augmented reality; information extraction; information retrieval; image processing; biostatistics; bioinformatics; mathematics for computer science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Sensors Section ‘Sensing and Imaging’ is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions and recommendations from the EBMs.

We are seeking original papers and review articles that showcase state-of-the-art theoretical and applicative advances, new experimental discoveries, and novel technological improvements regarding sensing and imaging. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be well promoted.

We would also like to take this opportunity to call on more experienced scholars to join the Section ‘Sensing and Imaging’ so that we can work together to further develop this exciting field of research.

Prof. Dr. Sylvain Girard
Prof. Dr. Christoph M. Friedrich
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. Sensors 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 2600 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

  • imaging systems
  • sensors
  • camera
  • radar and sonar
  • probes

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Published Papers (5 papers)

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Research

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27 pages, 2390 KiB  
Article
Visualizing Plant Responses: Novel Insights Possible Through Affordable Imaging Techniques in the Greenhouse
by Matthew M. Conley, Reagan W. Hejl, Desalegn D. Serba and Clinton F. Williams
Sensors 2024, 24(20), 6676; https://doi.org/10.3390/s24206676 - 17 Oct 2024
Viewed by 195
Abstract
Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations [...] Read more.
Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red–Green–Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = −0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = −0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = −0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = −0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations. Full article
(This article belongs to the Special Issue Feature Papers in Sensing and Imaging 2024)
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15 pages, 2913 KiB  
Article
Photobleaching Effect on the Sensitivity Calibration at 638 nm of a Phosphorus-Doped Single-Mode Optical Fiber Dosimeter
by Fiammetta Fricano, Adriana Morana, Martin Roche, Alberto Facchini, Gilles Mélin, Florence Clément, Nicolas Balcon, Julien Mekki, Emmanuel Marin, Youcef Ouerdane, Aziz Boukenter, Thierry Robin and Sylvain Girard
Sensors 2024, 24(17), 5547; https://doi.org/10.3390/s24175547 - 27 Aug 2024
Viewed by 474
Abstract
We investigated the influence of the photobleaching (PB) effect on the dosimetry performances of a phosphosilicate single-mode optical fiber (core diameter of 6.6 µm) operated at 638 nm, within the framework of the LUMINA project. Different irradiation tests were performed under ~40 keV [...] Read more.
We investigated the influence of the photobleaching (PB) effect on the dosimetry performances of a phosphosilicate single-mode optical fiber (core diameter of 6.6 µm) operated at 638 nm, within the framework of the LUMINA project. Different irradiation tests were performed under ~40 keV mean energy fluence X-rays at a 530 µ Gy(SiO2)/s dose rate to measure in situ the radiation-induced attenuation (RIA) growth and decay kinetics while injecting a 638 nm laser diode source with powers varying from 500 nW to 1 mW. For injected continuous power values under 1 µW, we did not measure any relevant influence of the photobleaching effect on the fiber radiation sensitivity coefficient of ~140 dB km−1 Gy−1 up to ~30 Gy. Above 1 µW, the fiber radiation sensitivity is significantly reduced due to the PB associated with the signal and can decrease to ~80 dB km−1 Gy−1 at 1 mW, strongly affecting the capability of this fiber to serve as a dosimeter-sensitive element. Higher power values up to 50 µW can still be used by properly choosing a pulsed regime with periodic injection cycles to reduce the PB efficiency and maintain the dosimetry properties. Basing on the acquired data, a simple model of the photobleaching effect on a coil of the investigated fiber is proposed in order to estimate its sensitivity coefficient evolution as a function of the cumulated dose and its fiber length when injecting a certain laser power. Additional studies need to investigate the influence of the temperature and the dose rate on the PB effects since these parameters were fixed during all the reported acquisitions. Full article
(This article belongs to the Special Issue Feature Papers in Sensing and Imaging 2024)
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29 pages, 9384 KiB  
Article
Occupancy Estimation from Blurred Video: A Multifaceted Approach with Privacy Consideration
by Md Sakib Galib Sourav, Ehsan Yavari, Xiaomeng Gao, James Maskrey, Yao Zheng, Victor M. Lubecke and Olga Boric-Lubecke
Sensors 2024, 24(12), 3739; https://doi.org/10.3390/s24123739 - 8 Jun 2024
Viewed by 665
Abstract
Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional [...] Read more.
Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings’ energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants’ privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants’ privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants’ visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants’ visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system. Full article
(This article belongs to the Special Issue Feature Papers in Sensing and Imaging 2024)
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16 pages, 6341 KiB  
Article
Spectral Reconstruction from RGB Imagery: A Potential Option for Infinite Spectral Data?
by Abdelhamid N. Fsian, Jean-Baptiste Thomas, Jon Y. Hardeberg and Pierre Gouton
Sensors 2024, 24(11), 3666; https://doi.org/10.3390/s24113666 - 5 Jun 2024
Viewed by 1433
Abstract
Spectral imaging has revolutionisedvarious fields by capturing detailed spatial and spectral information. However, its high cost and complexity limit the acquisition of a large amount of data to generalise processes and methods, thus limiting widespread adoption. To overcome this issue, a body of [...] Read more.
Spectral imaging has revolutionisedvarious fields by capturing detailed spatial and spectral information. However, its high cost and complexity limit the acquisition of a large amount of data to generalise processes and methods, thus limiting widespread adoption. To overcome this issue, a body of the literature investigates how to reconstruct spectral information from RGB images, with recent methods reaching a fairly low error of reconstruction, as demonstrated in the recent literature. This article explores the modification of information in the case of RGB-to-spectral reconstruction beyond reconstruction metrics, with a focus on assessing the accuracy of the reconstruction process and its ability to replicate full spectral information. In addition to this, we conduct a colorimetric relighting analysis based on the reconstructed spectra. We investigate the information representation by principal component analysis and demonstrate that, while the reconstruction error of the state-of-the-art reconstruction method is low, the nature of the reconstructed information is different. While it appears that the use in colour imaging comes with very good performance to handle illumination, the distribution of information difference between the measured and estimated spectra suggests that caution should be exercised before generalising the use of this approach. Full article
(This article belongs to the Special Issue Feature Papers in Sensing and Imaging 2024)
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Review

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21 pages, 449 KiB  
Review
Three-Dimensional Dense Reconstruction: A Review of Algorithms and Datasets
by Yangming Lee
Sensors 2024, 24(18), 5861; https://doi.org/10.3390/s24185861 - 10 Sep 2024
Viewed by 1027
Abstract
Three-dimensional dense reconstruction involves extracting the full shape and texture details of three-dimensional objects from two-dimensional images. Although 3D reconstruction is a crucial and well-researched area, it remains an unsolved challenge in dynamic or complex environments. This work provides a comprehensive overview of [...] Read more.
Three-dimensional dense reconstruction involves extracting the full shape and texture details of three-dimensional objects from two-dimensional images. Although 3D reconstruction is a crucial and well-researched area, it remains an unsolved challenge in dynamic or complex environments. This work provides a comprehensive overview of classical 3D dense reconstruction techniques, including those based on geometric and optical models, as well as approaches leveraging deep learning. It also discusses the datasets used for deep learning and evaluates the performance and the strengths and limitations of deep learning methods on these datasets. Full article
(This article belongs to the Special Issue Feature Papers in Sensing and Imaging 2024)
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