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Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and...
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Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (23 August 2023) | Viewed by 17755

Special Issue Editors

Dr. Wenfeng Zheng

E-Mail Website
Guest Editor
School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: surgical robot; AI/ML; haptics; teleoperation; medical robotics; image fusion; surgical vision; 3D visualization; adaptive visualization; artificial neural network; geoinformatics (GIS); artificial intelligence; computer graphics; motion tracking; image processing; machine vision; 3D reconstruction; medical imaging; robotic surgery; data mining; earth surface process; cognitive intelligence; GIS/RS; visual reasoning; visual question answering; cloud computing; perception and cognition, etc.
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mingzhe Liu

E-Mail Website
Guest Editor
College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
Interests: image and video processing; machine learning and deep learning; data mining and big data; intelligent information processing; information security; data science; artificial intelligence; blockchain; nuclear measurement and control technology; system control.
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Liu

E-Mail Website
Guest Editor
French National Center for Scientific Research (CNRS), LIRMM, 34095 Montpellier, France
Interests: visual augmentation and reconstruction; 3D reconstruction of deformable surface; haptics in human–machine interactions; multimodal sensor-based analysis of manipulation skills; surgical robot; medical image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Dan Wang

E-Mail Website
Guest Editor
Department of Internal Medicine, Division of Nephrology, The Ohio State University, Columbus, OH 43210, USA
Interests: kidney disease; cardiovascular diseases; microfluidic devices; sensors; tissue mechanical properties; glomerular filtration barrier
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past several decades, artificial intelligence (AI) has been tremendously boosted by new algorithm designs, exponentially increased computing power, and an immense volume of calculation materials (i.e., data). Nevertheless, appropriate feature fusion and high-level and abstract forms of knowledge representation are required to help AI to achieve better results, as the primary goal of AI research is to enable machines to perform complex tasks that would typically require human intelligence.

Restoration and enhancement techniques for perception are active research areas in robotics, which play essential roles in helping us to perceive and understand the world, including human activity recognition, surgical medicine, geoinformatics, and remote sensing analysis.

Artificial intelligence based on computer vision has been greatly strengthened and developed and has become one of the most important development areas of robotics. Object recognition, classification, segmentation, topology, network, efficiency, navigation. and search based on spatial attributes are also anticipated to become important and valuable fields of development in artificial intelligence and robotics in the future.

Recently, intelligent reasoning has been used widely to address the significant technical issues involved in implementing AI in real-world applications, such as intelligent medical care, environmental analysis and prediction, autonomous driving, intelligent transportation, text classification, recommended systems, machine translation, and analog dialogues.

In this Special Issue, we seek groundbreaking research and case studies that demonstrate future applications and advances in artificial intelligence and robotics, especially visual- and spatial-based perception enhancement and reasoning. Relevant topics include but are not limited to the following:

  • Artificial intelligence;
  • Robotics;
  • AI and machine learning in image processing;
  • Visual question answering (VQA) and visual reasoning;
  • Geospatial artificial intelligence, geospatial AI (GeoAI);
  • AI in remote sensing, geoinformatics, and spatiotemporal simulation;
  • AI for geospatial data acquisition, analysis, planning, and prediction;
  • Visual- and spatial-based perception enhancement and reasoning;
  • Visual augmentation and reconstruction, 3D reconstruction of deformable surfaces;
  • Medical image (such as CT, MRI, and ultrasound) processing;
  • Video-based activity recognition.

Dr. Wenfeng Zheng
Prof. Dr. Mingzhe Liu
Dr. Chao Liu
Dr. Dan Wang
Guest Editors

 

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Published Papers (8 papers)

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Editorial

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4 pages, 194 KiB  
Editorial
Future AI and Robotics: Visual and Spatial Perception Enhancement and Reasoning
by Wenfeng Zheng, Mingzhe Liu, Chao Liu and Dan Wang
Electronics 2023, 12(23), 4787; https://doi.org/10.3390/electronics12234787 - 26 Nov 2023
Viewed by 2545
Abstract
Over the past several decades, artificial intelligence (AI) has been tremendously boosted by new algorithm designs, exponentially increased computing power, and an immense volume of calculation materials (i [...] Full article
(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)

Research

Jump to: Editorial

21 pages, 12584 KiB  
Article
Attention-Guided HDR Reconstruction for Enhancing Smart City Applications
by Yung-Yao Chen, Chih-Hsien Hsia, Sin-Ye Jhong and Chin-Feng Lai
Electronics 2023, 12(22), 4625; https://doi.org/10.3390/electronics12224625 - 12 Nov 2023
Viewed by 1499
Abstract
In the context of smart city development, video surveillance serves as a critical component for maintaining public safety and operational efficiency. However, traditional surveillance systems are often constrained by a limited dynamic range, leading to the loss of essential image details. To address [...] Read more.
In the context of smart city development, video surveillance serves as a critical component for maintaining public safety and operational efficiency. However, traditional surveillance systems are often constrained by a limited dynamic range, leading to the loss of essential image details. To address this limitation, this paper introduces HDRFormer, an innovative framework designed to enhance high dynamic range (HDR) image quality in edge–cloud-based video surveillance systems. Leveraging advanced deep learning algorithms and Internet of Things (IoT) technology, HDRFormer employs a unique architecture comprising a feature extraction module (FEM) and a weighted attention module (WAM). The FEM leverages a transformer-based hierarchical structure to adeptly capture multi-scale image information. In addition, the guided filters are utilized to steer the network, thereby enhancing the structural integrity of the images. On the other hand, the WAM focuses on reconstructing saturated areas, improving the perceptual quality of the images, and rendering the reconstructed HDR images with naturalness and color saturation. Extensive experiments on multiple HDR image reconstruction datasets demonstrate HDRFormer’s substantial improvements, achieving up to a 2.7 dB increase in the peak signal-to-noise ratio (PSNR) and an enhancement of 0.09 in the structural similarity (SSIM) compared to existing methods. In addition, the framework exhibits outstanding performance in multi-scale structural similarity (MS-SSIM) and HDR visual difference predictor (HDR-VDP2.2). The proposed method not only outperforms the existing HDR reconstruction techniques but also offers better generalization capabilities, laying a robust foundation for future applications in smart cities. Full article
(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)
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19 pages, 8760 KiB  
Article
Three-Dimensional Point Cloud Reconstruction Method of Cardiac Soft Tissue Based on Binocular Endoscopic Images
by Jiawei Tian, Botao Ma, Siyu Lu, Bo Yang, Shan Liu and Zhengtong Yin
Electronics 2023, 12(18), 3799; https://doi.org/10.3390/electronics12183799 - 8 Sep 2023
Cited by 4 | Viewed by 1641
Abstract
Three-dimensional reconstruction technology based on binocular stereo vision is a key research area with potential clinical applications. Mainstream research has focused on sparse point reconstruction within the soft tissue domain, limiting the comprehensive 3D data acquisition required for effective surgical robot navigation. This [...] Read more.
Three-dimensional reconstruction technology based on binocular stereo vision is a key research area with potential clinical applications. Mainstream research has focused on sparse point reconstruction within the soft tissue domain, limiting the comprehensive 3D data acquisition required for effective surgical robot navigation. This study introduces a new paradigm to address existing challenges. An innovative stereoscopic endoscopic image correction algorithm is proposed, exploiting intrinsic insights into stereoscopic calibration parameters. The synergy between the stereoscopic endoscope parameters and the disparity map derived from the cardiac soft tissue images ultimately leads to the acquisition of precise 3D points. Guided by deliberate filtering and optimization methods, the triangulation process subsequently facilitates the reconstruction of the complex surface of the cardiac soft tissue. The experimental results strongly emphasize the accuracy of the calibration algorithm, confirming its utility in stereoscopic endoscopy. Furthermore, the image rectification algorithm exhibits a significant reduction in vertical parallax, which effectively enhances the stereo matching process. The resulting 3D reconstruction technique enables the targeted surface reconstruction of different regions of interest in the cardiac soft tissue landscape. This study demonstrates the potential of binocular stereo vision-based 3D reconstruction techniques for integration into clinical settings. The combination of joint calibration algorithms, image correction innovations, and precise tissue reconstruction enhances the promise of improved surgical precision and outcomes in the field of cardiac interventions. Full article
(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)
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17 pages, 8069 KiB  
Article
Dose Images Reconstruction Based on X-ray-Induced Acoustic Computed Tomography
by Yanhua Liu, Mingzhe Liu, Xin Jiang, Xianghe Liu and Min Liu
Electronics 2023, 12(10), 2241; https://doi.org/10.3390/electronics12102241 - 15 May 2023
Cited by 1 | Viewed by 1769
Abstract
The accurate reconstruction of the in vivo dose is a critical step in radiation therapy. X-ray-induced acoustic imaging is a promising technology for in vivo dose reconstruction, as it enables the nonradiative and noninvasive monitoring of radiation dose. However, current X-ray acoustic imaging [...] Read more.
The accurate reconstruction of the in vivo dose is a critical step in radiation therapy. X-ray-induced acoustic imaging is a promising technology for in vivo dose reconstruction, as it enables the nonradiative and noninvasive monitoring of radiation dose. However, current X-ray acoustic imaging methods suffer from several limitations, including high signal-to-noise ratio, poor imaging quality and massive loss of structural information. To address these limitations, we propose a dose image reconstruction method based on tensor sparse dictionary learning. Specifically, we combine tensor coding with compressed sensing data, extend two-dimensional dictionary learning to three-dimensional by using tensor product, and then utilize the spatial information of X-ray acoustic signal more efficiently. To reduce the artifacts of reconstruction images caused by spare sampling, we design the alternate iterative solution of the tensor sparse coefficient and tensor dictionary. In addition, we build the X-ray-induced acoustic dose images reconstruction system, simulate the X-ray acoustic signals based on patients’ information from Sichuan Cancer Hospital, and then create the simulated datasets. Compared to some typical state-of-the art imaging methods, the experimental results demonstrate that our method can significantly improve the quality of reconstructed images and the accuracy of dose distribution. Full article
(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)
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15 pages, 3118 KiB  
Article
Heterogeneous Quasi-Continuous Spiking Cortical Model for Pulse Shape Discrimination
by Runxi Liu, Haoran Liu, Bo Yang, Borui Gu, Zhengtong Yin and Shan Liu
Electronics 2023, 12(10), 2234; https://doi.org/10.3390/electronics12102234 - 14 May 2023
Cited by 1 | Viewed by 1589
Abstract
The present study introduces the heterogeneous quasi-continuous spiking cortical model (HQC-SCM) method as a novel approach for neutron and gamma-ray pulse shape discrimination. The method utilizes specific neural responses to extract features in the falling edge and delayed fluorescence parts of radiation pulse [...] Read more.
The present study introduces the heterogeneous quasi-continuous spiking cortical model (HQC-SCM) method as a novel approach for neutron and gamma-ray pulse shape discrimination. The method utilizes specific neural responses to extract features in the falling edge and delayed fluorescence parts of radiation pulse signals. In addition, the study investigates the contributions of HQC-SCM’s parameters to its discrimination performance, leading to the development of an automatic parameter selection strategy. As HQC-SCM is a chaotic system, a genetic algorithm-based parameter optimization method was proposed to locate local optima of HQC-SCM’s parameter solutions efficiently and robustly in just a few iterations of evolution. The experimental results of this study demonstrate that the HQC-SCM method outperforms traditional and state-of-the-art pulse shape discrimination algorithms, including falling edge percentage slope, zero crossing, charge comparison, frequency gradient analysis, pulse-coupled neural network, and ladder gradient methods. The outstanding discrimination performance of HQC-SCM enables plastic scintillators to compete with liquid and crystal scintillators’ neutron and gamma-ray pulse shape discrimination ability. Additionally, the HQC-SCM method outperforms other methods when dealing with noisy radiation pulse signals. Therefore, it is an effective and robust approach that can be applied in radiation detection systems across various fields. Full article
(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)
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13 pages, 527 KiB  
Communication
Energy-Based MRI Semantic Augmented Segmentation for Unpaired CT Images
by Shengliang Cai, Chuyun Shen and Xiangfeng Wang
Electronics 2023, 12(10), 2174; https://doi.org/10.3390/electronics12102174 - 10 May 2023
Cited by 1 | Viewed by 1448
Abstract
The multimodal segmentation of medical images is essential for clinical applications as it allows medical professionals to detect anomalies, monitor treatment effectiveness, and make informed therapeutic decisions. However, existing segmentation methods depend on paired images of modalities, which may not always be available [...] Read more.
The multimodal segmentation of medical images is essential for clinical applications as it allows medical professionals to detect anomalies, monitor treatment effectiveness, and make informed therapeutic decisions. However, existing segmentation methods depend on paired images of modalities, which may not always be available in practical scenarios, thereby limiting their applicability. To address this challenge, current approaches aim to align modalities or generate missing modality images without a ground truth, which can introduce irrelevant texture details. In this paper, we propose the energy-basedsemantic augmented segmentation (ESAS) model, which employs the energy of latent semantic features from a supporting modality to enhance the segmentation performance on unpaired query modality data. The proposed ESAS model is a lightweight and efficient framework suitable for most unpaired multimodal image-learning tasks. We demonstrate the effectiveness of our ESAS model on the MM-WHS 2017 challenge dataset, where it significantly improved Dice accuracy for cardiac segmentation on CT volumes. Our results highlight the potential of the proposed ESAS model to enhance patient outcomes in clinical settings by providing a promising approach for unpaired multimodal medical image segmentation tasks. Full article
(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)
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21 pages, 10391 KiB  
Article
A Novel Architecture of a Six Degrees of Freedom Parallel Platform
by Qiuxiang Gu, Jiawei Tian, Bo Yang, Mingzhe Liu, Borui Gu, Zhengtong Yin, Lirong Yin and Wenfeng Zheng
Electronics 2023, 12(8), 1774; https://doi.org/10.3390/electronics12081774 - 9 Apr 2023
Cited by 58 | Viewed by 3643
Abstract
With the rapid development of the manufacturing industry, industrial automation equipment represented by computer numerical control (CNC) machine tools has put forward higher and higher requirements for the machining accuracy of parts. Compared with the multi-axis serial platform solution, the parallel platform solution [...] Read more.
With the rapid development of the manufacturing industry, industrial automation equipment represented by computer numerical control (CNC) machine tools has put forward higher and higher requirements for the machining accuracy of parts. Compared with the multi-axis serial platform solution, the parallel platform solution is theoretically more suitable for high-precision machining equipment. There are many parallel platform solutions, but not one can provide a common physical platform to test the effectiveness of a variety of control algorithms. To achieve the goals, this paper is based on the Stewart six degrees of freedom parallel platform, and it mainly studies the platform construction. This study completed the mechanical structure design of the parallel platform. Based on the microprogrammed control unit (MCU) + pre-driver chip + three-phase full bridge solution, we have completed the circuit design of the motor driver. We wrote the program of MCU to drive six parallel robotic arms as well as the program of the parallel platform control center on the PC, and we completed the system joint debugging. The closed-loop control effect of the parallel platform workspace pose is realized. Full article
(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)
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12 pages, 1215 KiB  
Article
Generalized Knowledge Distillation for Unimodal Glioma Segmentation from Multimodal Models
by Feng Xiong, Chuyun Shen and Xiangfeng Wang
Electronics 2023, 12(7), 1516; https://doi.org/10.3390/electronics12071516 - 23 Mar 2023
Cited by 7 | Viewed by 1964
Abstract
Gliomas, primary brain tumors arising from glial cells, can be effectively identified using Magnetic Resonance Imaging (MRI), a widely employed diagnostic tool in clinical settings. Accurate glioma segmentation, which is crucial for diagnosis and surgical intervention, can be achieved by integrating multiple MRI [...] Read more.
Gliomas, primary brain tumors arising from glial cells, can be effectively identified using Magnetic Resonance Imaging (MRI), a widely employed diagnostic tool in clinical settings. Accurate glioma segmentation, which is crucial for diagnosis and surgical intervention, can be achieved by integrating multiple MRI modalities that offer complementary information. However, limited access to multiple modalities in certain clinical contexts often results in suboptimal performance of glioma segmentation methods. This study introduces a novel generalized knowledge distillation framework designed to transfer multimodal knowledge from a teacher model to a unimodal student model via two distinct distillation strategies: segmentation graph distillation and cascade region attention distillation. The former enables the student to replicate the teacher’s softened output, whereas the latter facilitates extraction and learning of region feature information at various levels within the teacher model. Our evaluation of the proposed distillation strategies using the BraTS 2018 dataset confirms their superior performance in unimodal segmentation contexts compared with existing methods. Full article
(This article belongs to the Special Issue Future AI and Robotics: Visual- and Spatial-Based Perception Enhancement and Reasoning)
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