Advances in Intelligent Control and Image Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 39471

Special Issue Editors

Department of Electrical Engineering, National Central University, Zhongli 32001, Taiwan
Interests: fuzzy theory and application; neural network; robot; image processing
Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
Interests: intelligent control and system; robot; computer vision
LAAS-CNRS, FWI University, 31400 Toulouse, France
Interests: image processing; bioinformatics; AI

Special Issue Information

Dear Colleagues,

Due to the rapid development of intelligent technologies in recent years, intelligent control, image processing, computer vision, and AI-based methods have gained many applications in various fields. Numerous cutting-edge studies can be allowed through combining the aforementioned technologies. Intelligent control is a class of control theory that uses artificial intelligence, knowledge-based systems, and smart algorithms for dealing with complex problems. These techniques still have the potential for development and a big impact in many fields of engineering.

This Special Issue highlights innovative research and applications, addresses the problems related to intelligent control, image processing, and computer vision, and also investigates widespread intelligent systems and techniques, such as fuzzy logic, neural networks, and evolutionary algorithms. Novel contributions based on (but not limited to) the following topics are welcome.

  • Intelligent control, techniques, and applications;
  • Soft computing (fuzzy logic, neural networks, evolutionary approaches);
  • Image processing, computer vision, and their applications;
  • Robotics and its applications.

Prof. Dr. Wen-June Wang
Dr. Chung-Hsun Sun
Prof. Dr. Andrei Doncescu
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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

  • intelligent control
  • image processing
  • computer vision
  • engineering applications

Published Papers (17 papers)

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Research

18 pages, 3205 KiB  
Article
Speed Control of PMSM Based on Fuzzy Active Disturbance Rejection Control under Small Disturbances
by Qi Zhang and Caiyue Zhang
Appl. Sci. 2023, 13(19), 10775; https://doi.org/10.3390/app131910775 - 28 Sep 2023
Viewed by 770
Abstract
Permanent Magnet Synchronous Motors (PMSMs), with their simple design, small size, and high-power factor, are ideally suited for realizing high-power AC drives and are widely used in various industries. In this study, Fuzzy Active Disturbance Rejection Control (Fuzzy-ADRC) is used to control the [...] Read more.
Permanent Magnet Synchronous Motors (PMSMs), with their simple design, small size, and high-power factor, are ideally suited for realizing high-power AC drives and are widely used in various industries. In this study, Fuzzy Active Disturbance Rejection Control (Fuzzy-ADRC) is used to control the speed of the PMSM. When a slight external disturbance occurs, this control strategy maintains the suppression characteristics of the self-excited control for the disturbance and enhances its ability to compensate for the disturbance. First, a mathematical model was developed to study the surface mount PMSM. Then, a motor control simulation model was created using PI control, vector control, and other control methods. The verification results indicate that the improved Fuzzy-ADRC system performs well under both internal and external minor disturbances. It exhibits a faster dynamic response and reduced regulation time (0.026 s to 0.017 s) compared to the traditional ADRC system. Furthermore, it shows less overshoot (reduced from 70% to 2.9%) compared to the Sliding Mode Observer (SMO). Taken together, the improved Fuzzy-ADRC system is characterized by small steady-state error, high load capacity, and control accuracy. With the assistance of this control strategy, the system can track speed with high accuracy and possesses stronger anti-interference capability to mitigate load disturbances. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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14 pages, 43078 KiB  
Article
An Automatic Tomato Growth Analysis System Using YOLO Transfer Learning
by Keita Fukada, Kataru Hara, Jingyong Cai, Daichi Teruya, Ikuko Shimizu, Takatsugu Kuriyama, Katsumi Koga, Kosuke Sakamoto, Yoshiyuki Nakamura and Hironori Nakajo
Appl. Sci. 2023, 13(12), 6880; https://doi.org/10.3390/app13126880 - 06 Jun 2023
Viewed by 984
Abstract
In recent years, Japan’s agricultural industry has faced a number of challenges, including a decline in production due to a decrease in farmland area, a shortage of labor due to a decrease in the number of producers, and an aging population. Therefore, in [...] Read more.
In recent years, Japan’s agricultural industry has faced a number of challenges, including a decline in production due to a decrease in farmland area, a shortage of labor due to a decrease in the number of producers, and an aging population. Therefore, in recent years, smart agriculture using robots and IoT has been studied. A caliper is often used to analyze the growth of tomatoes in a plant factory, but this method may damage the stems and is also hard on the measurer. We developed a system that detects them through image analysis and measures the thickness of stems and the length between flower clusters and growing points. The camera device developed in this study costs about USD 150 and once installed, it does not need to be moved unless it malfunctions. The camera device reduces the effort required to analyze crop growth by about 80%. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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14 pages, 991 KiB  
Article
A Framework for Frugal Supervised Learning with Incremental Neural Networks
by Stephane Cholet and Emmanuel Biabiany
Appl. Sci. 2023, 13(9), 5489; https://doi.org/10.3390/app13095489 - 28 Apr 2023
Viewed by 918
Abstract
This study proposes an implementation of an incremental neural network (INN) that was initially designed for affective computing tasks. INNs are a family of machine learning algorithms that combine prototype-based classifiers with neural networks. They achieve state-of-the-art performance with less data than traditional [...] Read more.
This study proposes an implementation of an incremental neural network (INN) that was initially designed for affective computing tasks. INNs are a family of machine learning algorithms that combine prototype-based classifiers with neural networks. They achieve state-of-the-art performance with less data than traditional approaches. In this research, we conduct an in-depth review of INN mechanisms and present a research-grade framework that enables the use of INNs on arbitrary data. We evaluated our implementation on two different datasets, including the AVEC2014 Challenge, which involved predicting depressive state from auditive and visual modalities. Our results are encouraging, demonstrating the potential of INNs in situations where approaches have to be explainable or when data are scarce. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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24 pages, 5014 KiB  
Article
Automatic Selective Encryption of DICOM Images
by Qamar Natsheh, Ana Sălăgean, Diwei Zhou and Eran Edirisinghe
Appl. Sci. 2023, 13(8), 4779; https://doi.org/10.3390/app13084779 - 11 Apr 2023
Cited by 2 | Viewed by 1507
Abstract
Securing DICOM images is essential to protect the privacy of patients, especially in the era of telemedicine and eHealth/mHealth. This increases the demand for rapid security. Nevertheless, a limited amount of research work has been conducted to ensure the security of DICOM images [...] Read more.
Securing DICOM images is essential to protect the privacy of patients, especially in the era of telemedicine and eHealth/mHealth. This increases the demand for rapid security. Nevertheless, a limited amount of research work has been conducted to ensure the security of DICOM images while minimizing the processing time. Hence, this paper introduces a selective encryption approach to reduce the processing time and sustain the robustness of security. The proposed approach selects regions within medical images automatically in the spatial domain using the pixel thresholding segmentation technique, then compresses and encrypts them using different encryption algorithms based on their importance. An adaptive two-region encryption approach is applied to single and multi-frame DICOM images, where the Region of Background (ROB) is encrypted using a light encryption algorithm, while the Region of Interest (ROI) is encrypted using a sophisticated encryption algorithm. For multi-frame DICOM images (Approach I), additional time-saving has been achieved by almost 10,000 times faster than the Naïve encryption approach, and 100 times better compression ratio, using one segmentation map based on a pre-defined reference frame for all the DICOM frames. For single-frame DICOM image (Approach II), a multi-region selective encryption approach is proposed, where the ROI is further split into three regions based on potential security threats, using a mathematical model that guarantees shorter encryption time in comparison with the Naive and the two-region encryption approaches, with almost 47% and 14% saving times, respectively. Based on the estimated processing time, Approach I outperformed Approach II noticeably. Further, cryptanalysis metrics are utilized to evaluate the proposed approaches, which indicate good robustness against a wide variety of attacks. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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15 pages, 3245 KiB  
Article
Use of Neural Networks and Computer Vision for Spill and Waste Detection in Port Waters: An Application in the Port of Palma (MaJorca, Spain)
by Mariano Morell, Pedro Portau, Antoni Perelló, Manuel Espino, Manel Grifoll and Carlos Garau
Appl. Sci. 2023, 13(1), 80; https://doi.org/10.3390/app13010080 - 21 Dec 2022
Cited by 1 | Viewed by 2146
Abstract
Water quality and pollution is the main environmental concern for ports and adjacent coastal waters. Therefore, the development of Port Environmental Management systems often relies on water pollution monitoring. Computer vision is a powerful and versatile tool for an exhaustive and systematic monitoring [...] Read more.
Water quality and pollution is the main environmental concern for ports and adjacent coastal waters. Therefore, the development of Port Environmental Management systems often relies on water pollution monitoring. Computer vision is a powerful and versatile tool for an exhaustive and systematic monitoring task. An investigation has been conducted at the Port of Palma de Mallorca (Spain) to assess the feasibility and evaluate the main opportunities and difficulties of the implementation of water pollution monitoring based on computer vision. Experiments on surface slicks and marine litter identification based on random image sets have been conducted. The reliability and development requirements of the method have been evaluated, concluding that computer vision is suitable for these monitoring tasks. Several computer vision techniques based on convolutional neural networks were assessed, finding that Image Classification is the most adequate for marine pollution monitoring tasks due to its high accuracy rates and low training requirements. Image set size for initial training and the possibility to improve accuracy through retraining with increased image sets were considered due to the difficulty in obtaining port spill images. Thus, we have found that progressive implementation can not only offer functional monitoring systems in a shorter time frame but also reduce the total development cost for a system with the same accuracy level. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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17 pages, 9899 KiB  
Article
Consecutive and Effective Facial Masking Using Image-Based Bone Sensing for Remote Medicine Education
by Sinan Chen, Masahide Nakamura and Kenji Sekiguchi
Appl. Sci. 2022, 12(20), 10507; https://doi.org/10.3390/app122010507 - 18 Oct 2022
Viewed by 1034
Abstract
Unlike masking human faces from images, facial masking in real-time, frame by frame from a video stream, presents technical challenges related to various factors such as camera-to-human distance, head direction, and mosaic schemes. In many existing studies, expensive equipment and huge computational resources [...] Read more.
Unlike masking human faces from images, facial masking in real-time, frame by frame from a video stream, presents technical challenges related to various factors such as camera-to-human distance, head direction, and mosaic schemes. In many existing studies, expensive equipment and huge computational resources are strongly required, and it is not easy to effectively realize real-time facial masking with a simpler approach. This study aims to develop a secure streaming system to support remote medicine education and to quantitatively evaluate consecutive and effective facial masking using image-based bone sensing. Our key idea is to use the facial feature of bone sensing instead of general face recognition techniques to perform facial masking from the video stream. We use a general-purpose computer and a USB fixed-point camera to implement the eye line mosaic and face mosaic. We quantitatively evaluate the results of facial masking at different distances and human head orientations using bone sensing technology and a depth camera. we compare the results of a similar approach for face recognition with those of bone sensing. As the main results, consecutive face masking using bone sensing is unaffected by distance and head orientation, and the variation width of the mosaic area is stable within around 30% of the target area. However, about three-fourths of the results using conventional face recognition were unable to mask their faces consecutively. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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12 pages, 5225 KiB  
Article
X-ray Image Enhancement Based on Adaptive Gradient Domain Guided Image Filtering
by Liangliang Li, Ming Lv, Hongbing Ma, Zhenhong Jia, Xinghua Yang and Weiyi Yang
Appl. Sci. 2022, 12(20), 10453; https://doi.org/10.3390/app122010453 - 17 Oct 2022
Cited by 1 | Viewed by 1940
Abstract
Due to the contrast of X-ray images being low, significant elements including organs, bones, and nodules are very difficult to identify, so contrast enhancement is necessary. In this paper, an X-ray image enhancement algorithm based on adaptive gradient domain guided image filtering is [...] Read more.
Due to the contrast of X-ray images being low, significant elements including organs, bones, and nodules are very difficult to identify, so contrast enhancement is necessary. In this paper, an X-ray image enhancement algorithm based on adaptive gradient domain guided image filtering is proposed. The amplification factor in the gradient domain guided image filtering needs to be set manually; it needs to constantly adjust the parameters to achieve the best enhancement effect, and this also increases the computational complexity. In order to solve this problem, an adaptive amplification factor is defined in this paper, and the proposed algorithm is applied to the X-ray image enhancement. Experimental results demonstrate that the proposed method is superior to state-of-the art algorithms in terms of detail enhancement and edge-preserving. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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14 pages, 2354 KiB  
Article
Data-Driven Robust Control Using Reinforcement Learning
by Phuong D. Ngo, Miguel Tejedor and Fred Godtliebsen
Appl. Sci. 2022, 12(4), 2262; https://doi.org/10.3390/app12042262 - 21 Feb 2022
Viewed by 2176
Abstract
This paper proposes a robust control design method using reinforcement learning for controlling partially-unknown dynamical systems under uncertain conditions. The method extends the optimal reinforcement learning algorithm with a new learning technique based on the robust control theory. By learning from the data, [...] Read more.
This paper proposes a robust control design method using reinforcement learning for controlling partially-unknown dynamical systems under uncertain conditions. The method extends the optimal reinforcement learning algorithm with a new learning technique based on the robust control theory. By learning from the data, the algorithm proposes actions that guarantee the stability of the closed-loop system within the uncertainties estimated also from the data. Control policies are calculated by solving a set of linear matrix inequalities. The controller was evaluated using simulations on a blood glucose model for patients with Type 1 diabetes. Simulation results show that the proposed methodology is capable of safely regulating the blood glucose within a healthy level under the influence of measurement and process noises. The controller has also significantly reduced the post-meal fluctuation of the blood glucose. A comparison between the proposed algorithm and the existing optimal reinforcement learning algorithm shows the improved robustness of the closed-loop system using our method. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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12 pages, 5905 KiB  
Article
Best Frame Selection to Enhance Training Step Efficiency in Video-Based Human Action Recognition
by Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, Marta Campos Ferreira, José J. M. Machado and João Manuel R. S. Tavares
Appl. Sci. 2022, 12(4), 1830; https://doi.org/10.3390/app12041830 - 10 Feb 2022
Cited by 8 | Viewed by 3418
Abstract
In recent years, with the growth of digital media and modern imaging equipment, the use of video processing algorithms and semantic film and image management has expanded. The usage of different video datasets in training artificial intelligence algorithms is also rapidly expanding in [...] Read more.
In recent years, with the growth of digital media and modern imaging equipment, the use of video processing algorithms and semantic film and image management has expanded. The usage of different video datasets in training artificial intelligence algorithms is also rapidly expanding in various fields. Due to the high volume of information in a video, its processing is still expensive for most hardware systems, mainly in terms of its required runtime and memory. Hence, the optimal selection of keyframes to minimize redundant information in video processing systems has become noteworthy in facilitating this problem. Eliminating some frames can simultaneously reduce the required computational load, hardware cost, memory and processing time of intelligent video-based systems. Based on the aforementioned reasons, this research proposes a method for selecting keyframes and adaptive cropping input video for human action recognition (HAR) systems. The proposed method combines edge detection, simple difference, adaptive thresholding and 1D and 2D average filter algorithms in a hierarchical method. Some HAR methods are trained with videos processed by the proposed method to assess its efficiency. The results demonstrate that the application of the proposed method increases the accuracy of the HAR system by up to 3% compared to random image selection and cropping methods. Additionally, for most cases, the proposed method reduces the training time of the used machine learning algorithm. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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17 pages, 3951 KiB  
Article
No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
by Domonkos Varga
Appl. Sci. 2022, 12(1), 101; https://doi.org/10.3390/app12010101 - 23 Dec 2021
Cited by 24 | Viewed by 5557
Abstract
No-reference image quality assessment (NR-IQA) has always been a difficult research problem because digital images may suffer very diverse types of distortions and their contents are extremely various. Moreover, IQA is also a very hot topic in the research community since the number [...] Read more.
No-reference image quality assessment (NR-IQA) has always been a difficult research problem because digital images may suffer very diverse types of distortions and their contents are extremely various. Moreover, IQA is also a very hot topic in the research community since the number and role of digital images in everyday life is continuously growing. Recently, a huge amount of effort has been devoted to exploiting convolutional neural networks and other deep learning techniques for no-reference image quality assessment. Since deep learning relies on a massive amount of labeled data, utilizing pretrained networks has become very popular in the literature. In this study, we introduce a novel, deep learning-based NR-IQA architecture that relies on the decision fusion of multiple image quality scores coming from different types of convolutional neural networks. The main idea behind this scheme is that a diverse set of different types of networks is able to better characterize authentic image distortions than a single network. The experimental results show that our method can effectively estimate perceptual image quality on four large IQA benchmark databases containing either authentic or artificial distortions. These results are also confirmed in significance and cross database tests. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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22 pages, 8125 KiB  
Article
Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing
by Yen-Cheng Huang, Chiung-An Chen, Tsung-Yi Chen, He-Sheng Chou, Wei-Chi Lin, Tzu-Chien Li, Jia-Jun Yuan, Szu-Yin Lin, Chun-Wei Li, Shih-Lun Chen, Yi-Cheng Mao, Patricia Angela R. Abu, Wei-Yuan Chiang and Wen-Shen Lo
Appl. Sci. 2021, 11(24), 11904; https://doi.org/10.3390/app112411904 - 14 Dec 2021
Cited by 8 | Viewed by 4436
Abstract
This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. [...] Read more.
This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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18 pages, 3861 KiB  
Article
High-Brightness Image Enhancement Algorithm
by Yifei Wei, Zhenhong Jia, Jie Yang and Nikola K. Kasabov
Appl. Sci. 2021, 11(23), 11497; https://doi.org/10.3390/app112311497 - 04 Dec 2021
Cited by 5 | Viewed by 1711
Abstract
In this paper, we introduce a tone mapping algorithm for processing high-brightness video images. This method can maximally recover the information of high-brightness areas and preserve detailed information. Along with benchmark data, real-life and practical application data were taken to test the proposed [...] Read more.
In this paper, we introduce a tone mapping algorithm for processing high-brightness video images. This method can maximally recover the information of high-brightness areas and preserve detailed information. Along with benchmark data, real-life and practical application data were taken to test the proposed method. The experimental objects were license plates. We reconstructed the image in the RGB channel, and gamma correction was carried out. After that, local linear adjustment was completed through a tone mapping window to restore the detailed information of the high-brightness region. The experimental results showed that our algorithm could clearly restore the details of high-brightness local areas. The processed image conformed to the visual effect observed by human eyes but with higher definition. Compared with other algorithms, the proposed algorithm has advantages in terms of both subjective and objective evaluation. It can fully satisfy the needs in various practical applications. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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10 pages, 2231 KiB  
Article
Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
by Ruolin Dong and Xiaodong Na
Appl. Sci. 2021, 11(23), 11145; https://doi.org/10.3390/app112311145 - 24 Nov 2021
Cited by 5 | Viewed by 1487
Abstract
Soil salinization is the main reason for declining soil quality and a reduction in agricultural productivity. We derive the spatial distribution of soil moisture from the temperature vegetation dryness index (TVDI) of Landsat TM-8 OLI images to analyze the effect of spatial heterogeneity [...] Read more.
Soil salinization is the main reason for declining soil quality and a reduction in agricultural productivity. We derive the spatial distribution of soil moisture from the temperature vegetation dryness index (TVDI) of Landsat TM-8 OLI images to analyze the effect of spatial heterogeneity of soil moisture on the retrieval accuracy of soil salinity. We establish five soil salinity inversion models for different soil moisture levels (drought levels) based on the canopy response salinity index (CRSI), normalized difference vegetation index (NDVI), and automatic water extraction index (AWEI) derived from Landsat TM-8 OLI images. The inversion accuracy of soil salinity is assessed using 42 field samples. The results show that the average accuracies of the five inversion models are higher than that of the traditional soil salinity inversion model of the entire study area. The proposed model underestimates soil salinity in high-moisture areas and overestimates it in drought areas. Therefore, inversion models of soil salinization should consider spatial differences in soil moisture to improve the inversion accuracy. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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20 pages, 16281 KiB  
Article
Underexposed Vision-Based Sensors’ Image Enhancement for Feature Identification in Close-Range Photogrammetry and Structural Health Monitoring
by Luna Ngeljaratan and Mohamed A. Moustafa
Appl. Sci. 2021, 11(23), 11086; https://doi.org/10.3390/app112311086 - 23 Nov 2021
Cited by 5 | Viewed by 1919
Abstract
This paper describes an alternative structural health monitoring (SHM) framework for low-light settings or dark environments using underexposed images from vision-based sensors based on the practical implementation of image enhancement algorithms. The proposed framework was validated by two experimental works monitored by two [...] Read more.
This paper describes an alternative structural health monitoring (SHM) framework for low-light settings or dark environments using underexposed images from vision-based sensors based on the practical implementation of image enhancement algorithms. The proposed framework was validated by two experimental works monitored by two vision systems under ambient lights without assistance from additional lightings. The first experiment monitored six artificial templates attached to a sliding bar that was displaced by a standard one-inch steel block. The effect of image enhancement in the feature identification and bundle adjustment integrated into the close-range photogrammetry were evaluated. The second validation was from a seismic shake table test of a full-scale three-story building tested at E-Defense in Japan. Overall, this study demonstrated the efficiency and robustness of the proposed image enhancement framework in (i) modifying the original image characteristics so the feature identification algorithm is capable of accurately detecting, locating and registering the existing features on the object; (ii) integrating the identified features into the automatic bundle adjustment in the close-range photogrammetry process; and (iii) assessing the measurement of identified features in static and dynamic SHM, and in structural system identification, with high accuracy. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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21 pages, 7272 KiB  
Article
Generation of a Panorama Compatible with the JPEG 360 International Standard Using a Single PTZ Camera
by Faiz Ullah, Oh-Jin Kwon and Seungcheol Choi
Appl. Sci. 2021, 11(22), 11019; https://doi.org/10.3390/app112211019 - 21 Nov 2021
Cited by 2 | Viewed by 1916
Abstract
Recently, the JPEG working group (ISO/IEC JTC1 SC29 WG1) developed an international standard, JPEG 360, that specifies the metadata and functionalities for saving and sharing 360-degree images efficiently to create a more realistic environment in various virtual reality services. We surveyed the metadata [...] Read more.
Recently, the JPEG working group (ISO/IEC JTC1 SC29 WG1) developed an international standard, JPEG 360, that specifies the metadata and functionalities for saving and sharing 360-degree images efficiently to create a more realistic environment in various virtual reality services. We surveyed the metadata formats of existing 360-degree images and compared them to the JPEG 360 metadata format. We found that existing omnidirectional cameras and stitching software packages use formats that are incompatible with the JPEG 360 standard to embed metadata in JPEG image files. This paper proposes an easy-to-use tool for embedding JPEG 360 standard metadata for 360-degree images in JPEG image files using a JPEG-defined box format: the JPEG universal metadata box format. The proposed implementation will help 360-degree cameras and software vendors provide immersive services to users in a standardized manner for various markets, such as entertainment, education, professional training, navigation, and virtual and augmented reality applications. We also propose and develop an economical JPEG 360 standard compatible panoramic image acquisition system from a single PTZ camera with a special-use case of a wide field of view image of a conference or meeting. A remote attendee of the conference/meeting can see the realistic and immersive environment through our PTZ panorama in virtual reality. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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20 pages, 6195 KiB  
Article
Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces
by Hsiang-Chieh Chen and Zheng-Ting Li
Appl. Sci. 2021, 11(22), 10966; https://doi.org/10.3390/app112210966 - 19 Nov 2021
Cited by 3 | Viewed by 1603
Abstract
This article introduces an automated data-labeling approach for generating crack ground truths (GTs) within concrete images. The main algorithm includes generating first-round GTs, pre-training a deep learning-based model, and generating second-round GTs. On the basis of the generated second-round GTs of the training [...] Read more.
This article introduces an automated data-labeling approach for generating crack ground truths (GTs) within concrete images. The main algorithm includes generating first-round GTs, pre-training a deep learning-based model, and generating second-round GTs. On the basis of the generated second-round GTs of the training data, a learning-based crack detection model can be trained in a self-supervised manner. The pre-trained deep learning-based model is effective for crack detection after it is re-trained using the second-round GTs. The main contribution of this study is the proposal of an automated GT generation process for training a crack detection model at the pixel level. Experimental results show that the second-round GTs are similar to manually marked labels. Accordingly, the cost of implementing learning-based methods is reduced significantly because data labeling by humans is not necessitated. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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21 pages, 25017 KiB  
Article
Design and Validation of a Virtual Chemical Laboratory—An Example of Natural Science in Elementary Education
by Chi-Yi Tsai, Yu-Chen Ho and Humaira Nisar
Appl. Sci. 2021, 11(21), 10070; https://doi.org/10.3390/app112110070 - 27 Oct 2021
Cited by 3 | Viewed by 2965
Abstract
In the natural science curriculum, chemistry is a very important domain. However, when conducting chemistry experiments, safety issues need to be taken seriously, and excessive material waste may be caused during the experiment. Based on the 11-year-old student science curriculum, this paper proposed [...] Read more.
In the natural science curriculum, chemistry is a very important domain. However, when conducting chemistry experiments, safety issues need to be taken seriously, and excessive material waste may be caused during the experiment. Based on the 11-year-old student science curriculum, this paper proposed a virtual chemistry laboratory, which was designed by combining a virtual experiment application with physical teaching materials. The virtual experiment application was a virtual experiment laboratory environment created by using selected experimental equipment cards in combination with augmented reality (AR) technology. The physical teaching materials included all virtual equipment required for experiment units. Each piece of equipment had corresponding cards for learners to choose from and utilize in specific experimental operations. It was hoped that students were able to achieve the desired learning effectiveness of experimental teaching while reducing the waste of experimental materials through the virtual experimental environment. This study employed the quasi-experimental and questionnaire survey methods to evaluate both learning effectiveness and learning motivation. Eighty-one students and eight elementary school teachers were surveyed as research subjects. The experimental results revealed that significant differences in learning effectiveness existed between the experimental group and control group, indicating that the application of AR technology to teaching substantively helped enhance students’ learning effectiveness and motivation. In addition, the results of the teacher questionnaire demonstrated that the virtual chemistry laboratory proposed in this study could effectively assist with classroom teaching. Full article
(This article belongs to the Special Issue Advances in Intelligent Control and Image Processing)
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