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Selected Papers from the 2018 IEEE International Conference on Imaging Systems and Techniques (IST 2018)

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

Deadline for manuscript submissions: closed (30 July 2019) | Viewed by 27448

Special Issue Editors

Dr. Pengda Hong

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Guest Editor
Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
Interests: imaging techniques; nano-measurement; remote sensing; image processing; image restoration; nonlinear optics; lasers; optical parametric oscillation; terahertz’s generation and applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrzej Skalski

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Guest Editor
Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: augumented reality; MR/AR/VR/XR; medical image analysis; image segmentation; image registration
Special Issues, Collections and Topics in MDPI journals
Dr. Lihui Peng

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Guest Editor
Department of Automation, Tsinghua University, Beijing, China
Interests: process tomography; measurement techniques for multiphase flow; flow measurement and instrumentation; inverse problems; multi-sensor data fusion
Prof. Dr. Ayman El-Baz

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Guest Editor
Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
Interests: artificial intelligence medical imaging; big data
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Prof. Dr. Michalis Zervakis

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Guest Editor
School of Electrical and Computer Engineering, Technical University of Crete, Chania 731 00, Greece
Interests: digital image and signal processing; biomedical applications
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Dr. Lijun Xu

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Guest Editor
School of Instrumentation Science and Opto-electronics Engineering, Beihang University, China
Interests: tomographic imaging; digital imaging and dynamic process monitoring
Prof. Dr. Wuqiang Yang

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Guest Editor
School of Electrical and Electronic Engineering, University of Manchester, Manchester, UK
Interests: sensors; instrumentation; electrical capacitance tomography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. George Giakos

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Guest Editor
Department of Electrical and Computer Engineering, Manhattan College, Riverdale, NY 10471, USA
Interests: image technology innovation through integration of physics, engineering and computational science; cognitive computing and imaging; retina vision sensors; bioinspired photonics; autonomous navigation; distributed vision systems; Industry 4; artificial intelligence

Special Issue Information

Dear Colleagues,

The 2018 IEEE International Conference on Imaging Systems and Techniques is the premier forum for the presentation of technological advances and research results and will take place jointly with the IEEE International School of Imaging in Kraków, Poland, 16–18 October, 2018 http://ist2018.ieee-ims.org/. Selected original and invited research works presented at the conference will be organized into this Special Issue of Sensors, “Selected Papers from the 2018 IEEE International Conference on Imaging Systems and Techniques (IST 2018)”. Submissions should be accompanied by their original proceeding papers and should be substantially revised.

The conference is sponsored by the Institute of Electrical and Electronics Engineers (IEEE), which is the world’s largest professional association, with nearly 500,000 members, dedicated to advancing technological innovation and excellence for the benefits of humanity.

The scope of the IST 2018 includes, but is not limited to:

  1. Exploring, advancing, and generating new knowledge on multifaceted imaging design principles, systems, and techniques, with applications in medical imaging, genomics and artificial intelligence, aimed at the exploring of novel pathophysiology and metabolic mechanisms and measuring therapeutic efficacy
  2. Machine learning and data mining solutions utilizing medical imaging to assist clinicians and healthcare providers to bring big data to personalized medicine
  3. Imaging and cognitive machine vision systems, imaging informatics, robotic vision systems, with applications in Industry 4.0, healthcare, autonomous driving and navigation, Internet of Things (IoT), and space and resource exploration
  4. Emerging imaging trends that would lead ultimately to novel systems and technologies, standards and metrology, and systems with unsurpassable image quality, scalability, and miniaturization capabilities

The 2018 IEEE International Conference on Imaging Systems and Technique aims to provide a forum for prestigious specialists and scholars to share their experiences and demonstrate frontier research results in all respects of imaging technologies, systems and techniques.

Engineers, and scientists from industry, government, academia, and healthcare who want to report novel scientific results, technological and clinical advances in the multidisciplinary areas of imaging systems, are invited to attend the IST Conference and interact with major worldwide experts.

Dr. Pengda Hong
Dr. Andrzej Skalski
Dr. Lihui Peng
Dr. Ayman El-baz
Dr. Michalis Zervakis
Dr. Lijun Xu
Dr. Wuqiang Yang
Dr. George Giakos
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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

13 pages, 2337 KiB  
Article
A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings
by Marcin Kopaczka, Lukas Breuer, Justus Schock and Dorit Merhof
Sensors 2019, 19(19), 4135; https://doi.org/10.3390/s19194135 - 24 Sep 2019
Cited by 21 | Viewed by 4101
Abstract
We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into [...] Read more.
We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions. Full article
(This article belongs to the Special Issue Selected Papers from the 2018 IEEE International Conference on Imaging Systems and Techniques (IST 2018))
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18 pages, 6997 KiB  
Article
Investigation of Multi-Plane Scheme for Compensation of Fringe Effect of Electrical Resistance Tomography Sensor
by Wenbin Tian, Xiaofeng Liang, Xiaolei Qu, Jiangtao Sun, Shuo Gao, Lijun Xu and Wuqiang Yang
Sensors 2019, 19(14), 3132; https://doi.org/10.3390/s19143132 - 16 Jul 2019
Cited by 3 | Viewed by 3149
Abstract
Conventional electrical resistance tomography (ERT) sensors suffer from the fringe effect, i.e., severe distortion of the electric field on both ends of the measurement electrodes, leading to a 3D sensing region for a 2D sensor. As a result, the objects outside an ERT [...] Read more.
Conventional electrical resistance tomography (ERT) sensors suffer from the fringe effect, i.e., severe distortion of the electric field on both ends of the measurement electrodes, leading to a 3D sensing region for a 2D sensor. As a result, the objects outside an ERT sensor plane affect the sensing and hence image, i.e., deteriorating the image quality. To address this issue, a multiple-plane ERT sensor scheme is proposed in this paper. With this scheme, auxiliary sensor planes are used to provide references for the fringe effect of the measurement plane, for compensation by subtracting the weighed influence of the fringe effect. Simulation results show that the proposed scheme, either three-plane or two-plane sensor, can compensate for the fringe effect induced by objects outside the measurement plane with a variety of axial object distributions, i.e., several non-conductive bars or conductive bars placed at different cross-sectional and axial positions inside the sensor. Experiments were carried out. Images obtained with single-plane and multiple-plane ERT sensors are compared, and the proposed compensation scheme has been hence verified. Full article
(This article belongs to the Special Issue Selected Papers from the 2018 IEEE International Conference on Imaging Systems and Techniques (IST 2018))
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20 pages, 9982 KiB  
Article
Robust Shelf Monitoring Using Supervised Learning for Improving On-Shelf Availability in Retail Stores
by Kyota Higa and Kota Iwamoto
Sensors 2019, 19(12), 2722; https://doi.org/10.3390/s19122722 - 17 Jun 2019
Cited by 18 | Viewed by 10121
Abstract
This paper proposes a method to robustly monitor shelves in retail stores using supervised learning for improving on-shelf availability. To ensure high on-shelf availability, which is a key factor for improving profits in retail stores, we focus on understanding changes in products regarding [...] Read more.
This paper proposes a method to robustly monitor shelves in retail stores using supervised learning for improving on-shelf availability. To ensure high on-shelf availability, which is a key factor for improving profits in retail stores, we focus on understanding changes in products regarding increases/decreases in product amounts on the shelves. Our method first detects changed regions of products in an image by using background subtraction followed by moving object removal. It then classifies the detected change regions into several classes representing the actual changes on the shelves, such as “product taken (decrease)” and “product replenished/returned (increase)”, by supervised learning using convolutional neural networks. It finally updates the shelf condition representing the presence/absence of products using classification results and computes the product amount visible in the image as on-shelf availability using the updated shelf condition. Three experiments were conducted using two videos captured from a surveillance camera on the ceiling in a real store. Results of the first and second experiments show the effectiveness of the product change classification in our method. Results of the third experiment show that our method achieves a success rate of 89.6% for on-shelf availability when an error margin is within one product. With high accuracy, store clerks can maintain high on-shelf availability, enabling retail stores to increase profits. Full article
(This article belongs to the Special Issue Selected Papers from the 2018 IEEE International Conference on Imaging Systems and Techniques (IST 2018))
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19 pages, 2078 KiB  
Article
Colour Constancy for Image of Non-Uniformly Lit Scenes
by Md Akmol Hussain, Akbar Sheikh-Akbari and Iosif Mporas
Sensors 2019, 19(10), 2242; https://doi.org/10.3390/s19102242 - 15 May 2019
Cited by 2 | Viewed by 3992
Abstract
Digital camera sensors are designed to record all incident light from a captured scene, but they are unable to distinguish between the colour of the light source and the true colour of objects. The resulting captured image exhibits a colour cast toward the [...] Read more.
Digital camera sensors are designed to record all incident light from a captured scene, but they are unable to distinguish between the colour of the light source and the true colour of objects. The resulting captured image exhibits a colour cast toward the colour of light source. This paper presents a colour constancy algorithm for images of scenes lit by non-uniform light sources. The proposed algorithm uses a histogram-based algorithm to determine the number of colour regions. It then applies the K-means++ algorithm on the input image, dividing the image into its segments. The proposed algorithm computes the Normalized Average Absolute Difference (NAAD) for each segment and uses it as a measure to determine if the segment has sufficient colour variations. The initial colour constancy adjustment factors for each segment with sufficient colour variation is calculated. The Colour Constancy Adjustment Weighting Factors (CCAWF) for each pixel of the image are determined by fusing the CCAWFs of the segments, weighted by their normalized Euclidian distance of the pixel from the center of the segments. Results show that the proposed method outperforms the statistical techniques and its images exhibit significantly higher subjective quality to those of the learning-based methods. In addition, the execution time of the proposed algorithm is comparable to statistical-based techniques and is much lower than those of the state-of-the-art learning-based methods. Full article
(This article belongs to the Special Issue Selected Papers from the 2018 IEEE International Conference on Imaging Systems and Techniques (IST 2018))
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10 pages, 3659 KiB  
Article
Analysis of Disparity Information for Depth Extraction Using CMOS Image Sensor with Offset Pixel Aperture Technique
by Byoung-Soo Choi, Jimin Lee, Sang-Hwan Kim, Seunghyuk Chang, JongHo Park, Sang-Jin Lee and Jang-Kyoo Shin
Sensors 2019, 19(3), 472; https://doi.org/10.3390/s19030472 - 24 Jan 2019
Cited by 8 | Viewed by 4991
Abstract
A complementary metal oxide semiconductor (CMOS) image sensor (CIS), using offset pixel aperture (OPA) technique, was designed and fabricated using the 0.11-µm CIS process. In conventional cameras, an aperture is located on the camera lens. However, in a CIS camera using OPA technique, [...] Read more.
A complementary metal oxide semiconductor (CMOS) image sensor (CIS), using offset pixel aperture (OPA) technique, was designed and fabricated using the 0.11-µm CIS process. In conventional cameras, an aperture is located on the camera lens. However, in a CIS camera using OPA technique, apertures are integrated as left-offset pixel apertures (LOPAs) and right-offset pixel apertures (ROPAs). A color pattern is built, comprising LOPA, blue, red, green, and ROPA pixels. The disparity information can be acquired from the LOPA and ROPA channels. Both disparity information and two-dimensional (2D) color information can be simultaneously acquired from the LOPA, blue, red, green, and ROPA channels. A geometric model of the OPA technique is constructed to estimate the disparity of the image, and the measurement results are compared with the estimated results. Depth extraction is thus achieved by a single CIS using the OPA technique, which can be easily adapted to commercial CIS cameras. Full article
(This article belongs to the Special Issue Selected Papers from the 2018 IEEE International Conference on Imaging Systems and Techniques (IST 2018))
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