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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Feng, Deying Yang, Jie Wei, Yanxia Xiao, Hairong Zhang, Laigang School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Liaocheng University China

ISBN: (纸本)9781450396110

We propose an unsupervised person search method for video surveillance. This method considers both the spatial features of persons within each frame and the temporal relationship of the same person among different frames. Thus, the spatial features are extracted by region convolutional neural network, and the temporal relationship is organized by gate recurrent unit. The spatio-temporal features are generated by the following average pooling layer and indexed by locality sensitive hashing. A surveillance video database is constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves the search accuracy by utilizing the spatio-temporal features. © 2022 ACM.

关键词： Monitoring

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MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects

arXiv

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arXiv 2022年

作者： Peng, Juewen Zhang, Jianming Luo, Xianrui Lu, Hao Xian, Ke Cao, Zhiguo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of AIA Huazhong University of Science and Technology China Adobe Research

Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background. However, it is challenging for existing bokeh rendering methods to simulate realistic partial occlusion effects due to the missing information of the occluded area in an all-in-focus image. Inspired by the learnable 3D scene representation, Multiplane image (MPI), we attempt to address the partial occlusion by introducing a novel MPI-based high-resolution bokeh rendering framework, termed MPIB. To this end, we first present an analysis on how to apply the MPI representation to bokeh rendering. Based on this analysis, we propose an MPI representation module combined with a background inpainting module to implement high-resolution scene representation. This representation can then be reused to render various bokeh effects according to the controlling parameters. To train and test our model, we also design a ray-tracing-based bokeh generator for data generation. Extensive experiments on synthesized and real-world images validate the effectiveness and flexibility of this framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning 13

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncer...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhao, Tingting Li, Zuoyong Quan, Li Zeng, Kun Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Computer Science and Big Data Fuzhou University Fuzhou350121 China

ISBN: (纸本)9798350319156

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance. © 2023 IEEE.

关键词： Textures

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Multi-Sequence MRI Thoracic Spine Compression Fracture Classification Based on Local-Global Feature Selection and Cross-Attention Fusion

Multi-Sequence MRI Thoracic Spine Compression Fracture Class...

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Advanced Algorithms and control Engineering (ICAACE), International Conference on

作者： Shuhua Jin Jinjin Hai Shijie Wei Jian Chen Kai Qiao Weicong Zhang Hai Lv Bin Yan Henan Key Laboratory of Imaging and Intelligent Processing School of Cyber Science and Engineering Zhengzhou University Information Engineering University Zhengzhou Henan China Henan Key Laboratory of Imaging and Intelligent Processing Information Engineering University Zhengzhou Henan China Henan Key Laboratory of Imaging and Intelligent Processing Information Engineering University Zhengzhou University Zhengzhou Henan China Jiangmen Key Laboratory of Artificial Intelligence in Medical Image Computation and Application Jiangmen Guangdong China Department of Spinal Surgery The Fifth Affiliated Hospital of Sun Yat-sen University Zhuhai Guangdong China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Thoracic vertebral compression fracture (VCF) is a common fracture type in clinical practice, which seriously affects the quality of life of patients. The benign and malignant classifications of VCF are of significant diagnostic importance for the selection of clinical treatment options. Magnetic resonance imaging (MRI), as the main diagnostic tool for VCF, has the characteristics of multi-sequence imaging, and the structural and tissue information provided by different sequences complement each other. This paper proposes a multi-sequence MRI feature fusion network CAMSF based on vision transformer (ViT), which solves the problems of limited local feature extraction of ViT and insufficient interaction between multi-sequence image features. This method combines a multi-head self-attention mechanism and a deep convolution module to effectively capture global and local features, and uses a graph pooling-based feature selection method to optimize redundant features. In addition, a fusion method based on the cross-attention mechanism is used to deeply interact with different MRI sequence features to obtain complementary information across sequence images. Experimental results on the thoracic VCF dataset show that the classification accuracy of CAMSF reaches 84.88% and the AUC reaches 83.39%, which is significantly better than the existing ViT-based multimodal image feature fusion method. The ablation experiment further verifies the effectiveness of each module, indicating that CAMSF can effectively fuse multi-sequence MRI features and provide an effective solution for the accurate classification of VCF.

关键词： Computer vision Accuracy image coding control engineering Convolution Magnetic resonance imaging Sensitivity and specificity Feature extraction Transformers Cancer

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Robust optimization-based fusion of GNSS and Visual-Inertial-Wheel Odometry

Robust optimization-based fusion of GNSS and Visual-Inertial...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Hou, Jake Jie Wu, Xinrui Shan, Jiwei Li, Dun Wang, Hesheng Shanghai Jiao Tong University Department of Automation China Shanghai Karlang Industrial Nantong Intelligent Technology Co. Ltd China Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

SLAM is significant to autonomous mobile robots. In order to realize drift-free state estimation and provide accurate localization results for the subsequent navigation, we develop an optimization-based framework consisting of a high-frequency Inertial-Wheel Odometry(IWO), Visual-Inertial-Wheel Odometry(VIWO) and a global fusion module. To fuse the local state with the position provided by GNSS, we perform ternary edge in factor graph optimization to estimate the transformation between the local frame and ENU coordinate. Thanks to the tightly-coupled VIWO, the localization property of the odometry presents more robustness, and improve the accuracy of global fusion. A noise repropagation strategy is introduced to ensure the consistency of the IMU and wheel odometer. We evaluate the proposed system on public datasets and test it in real-world scenario. The experimental results show that our system achieves accurate pose estimation in outdoor environments. © 2022 IEEE.

关键词： Frequency estimation

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Extended State Observer Based Fault-Tolerant Predictive control for a Four-Mecanum Wheels Mobile Robot 14

Extended State Observer Based Fault-Tolerant Predictive Cont...

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14th IEEE International Conference on Cyber Technology in Automation, control, and intelligent Systems, CYBER 2024

作者： Yang, Binghao Wang, Dongliang Wen, Ziling Wei, Wu Li, Wenji Fan, Zhun Shantou University Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong 515063 China Shantou University School of Department of Electronic and Information Engineering Guangdong 515063 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Guangzhou510640 China School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China

ISBN: (纸本)9798331506056

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment. © 2024 IEEE.

关键词： Mobile robots

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TSN-compatible Industrial Wired/Wireless Multi-protocol Conversion Mechanism and Module 48

TSN-compatible Industrial Wired/Wireless Multi-protocol Conv...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Chen, Yingxiu Xu, Qimin Zhang, Jinglong Xu, Lei Li, Lingzhi Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The interoperability of heterogeneous networks, including hybrid industrial wired/wireless protocols, is a vital aspect of the Industrial Internet of Things (IIoT) since various industrial protocols have coexisted. Therefore, it is hard to obtain a multiple protocol conversion solution with the flexible configuration. Moreover, the previous works do not adapt to the Time Sensitive Networking (TSN), which is considered as the potential technology of IIoT. To tackle the above problems, we proposed an architecture of the multiple conversion system supporting the conversion of multiple protocols. Then, we proposed a protocol conversion mechanism to improve the flexibility of multiple protocol conversion by separating into data and configuration planes. Moreover, we propose a TSN-compatible frame with dynamic priority mapping algorithm, which adjusts the priority in the VLAN tag by the processing delay. Finally, we develop a hardware module and system supporting multiple protocol conversion, which verify that the proposed architecture could satisfy the flexible conversion requirement through the experiments. © 2022 IEEE.

关键词： Ethernet

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A Convolutional Knowledge Tracing Method based on Graph Embedding

A Convolutional Knowledge Tracing Method based on Graph Embe...

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Computer Engineering and intelligent Communications (ISCEIC), International Symposium on

作者： Guimin Huang Ying Li Qingkai Guo Nanxiao Deng Jianxing Lin School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

With online education rapidly developing, it is a significant issue to evaluate students’ grasp of knowledge more accurately. Knowledge tracing models are good at it, in which convolutional knowledge tracing (CKT) based on convolutional neural networks(CNN) gets better performance. However, we found that the CKT model ignores the relation between the knowledge points, which we believe has an important impact on the correctness of students’ answers. Therefore, we put forward a graph embedding based convolutional knowledge tracing model (CKT-GE), which feeds the relation between the knowledge points with other inputs into model for improving the effectiveness of knowledge tracing. Our model outperforms existing models on two classic datasets, and improves the performance over the original CKT model, allowing for more accurate assessment of student performance.

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Multi-Attention Gate Based U-net for Retinal Vessel Segmentation 12

Multi-Attention Gate Based U-net for Retinal Vessel Segmenta...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Zhu, Yu Li, Rui Teng, Shenghua Cao, Xinrong Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao266590 China College of Computer and Control Engineering Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350310153

Retinal blood vessel segmentation images can be used to detect and evaluate various cardiovascular and ophthalmic diseases. However, due to the intricate vessel structures and blurred boundaries of vessels, it is a huge challenge to efficiently and accurately segment blood vessels. To deal with the above problems, this paper improves on the U-net by firstly using multi-scale feature convolution with kernels of varying size for feature extraction. Second, a non-local attention mechanism is applied to obtain richer global semantic information. Then multi-attention gate is used in the skip connection part by inputting feature maps of various scales and dimensions and selectively learning the interrelated regions, which improves the segmentation ability of the network model for the tiny structure of blood vessels. Quantitative and qualitative experimental results on two public datasets, DRIVE and CHASE-DB1, demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Blood vessels

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A Network Combining CNN and Transformer for Blind image Super-Resolution 12

A Network Combining CNN and Transformer for Blind Image Supe...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Zhang, Shuhao Li, Zuoyong Teng, Shenghua Zeng, Kun Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao266590 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Blind super-resolution (SR) requires not only estimating blur kernel, but also super-resolving low-resolution image based on estimated blur kernel. Most blind SR methods use convolutional neural networks (CNNs) for kernel estimation, which cannot exploit long-range dependency within image domain, thus failing to predict blur kernel accurately. To address this issue, we propose a network combining CNN and transformer named NCCT for kernel estimation. By modeling local and non-local image priors simultaneously, NCCT outperforms other blind SR methods in terms of kernel estimation accuracy. Moreover, we design a network module named RRFDB for constructing lightweight blind SR network, which runs faster and achieves comparative SR performance with fewer parameters compared with other state-of-the-art blind SR methods. © 2022 IEEE.

关键词： Optical resolving power

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