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6th Conference on Frontiers in Optical Imaging and Technology: Applications of Imaging Technologies

作者： Zhenshuai, Chen Hengjia, Qin Wei, Xiang Yunpeng, Liu Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China The Third Military Representative Office The Air Force Equipment Department in Shenyang Region Shenyang110027 China

ISBN: (数字)9781510679757

ISBN: (纸本)9781510679740

In the field of remote sensing image interpretation, utilizing Convolutional Neural Networks (CNNs) for building extraction is a highly significant task. End-to-end building extraction methods typically consist of two key components: the encoder and the decoder. However, during building extraction facilitated by the encoder, down-sampling operations often lead to a loss of boundary features in the segmented objects. Many of these lost features correspond to the boundaries of buildings, and the reduction of smaller-scale boundaries features diminishes the network's attention to building boundary, resulting in blurred architectural delineation. In this paper, we propose the Reshape Feature Distribution (RFD-Net) network to alleviate the problem of boundary blurriness. We embed a reshaping feature distribution module within the network, which manipulates the data distribution of feature values by compressing the maximum values and elevating the minimum values. This module can effectively increase the magnitude of feature values at positions corresponding to building boundaries in the feature maps, subsequently enhancing the network's attention to building boundaries and alleviating the problem of boundary blurriness. We conducted experiments on the WHU dataset, demonstrating the effectiveness of our proposed approach. © 2024 SPIE.

关键词： Buildings

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive Control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Guanfeng Yu Han Zhang Zehao Wang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities.

关键词： Pedestrians Accuracy Systematics Wheelchairs Estimation Predictive models Real-time systems

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control For Tractor-trailer Trajectory Tracking With Unknown Parameters

K-BMPC: Derivative-based Koopman Bilinear Model Predictive C...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zehao Wang Han Zhang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive Control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.

关键词： Trajectory tracking Computational modeling Estimation Predictive models Control systems Data models Nonlinear dynamical systems

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Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification

arXiv

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arXiv 2025年

作者： Yang, Wennuo Wu, Shiling Zhou, Yuzhi Luo, Cheng He, Xilin Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China HBUG Lab University of Exeter United Kingdom

Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationship among variables in MTS usually contain crucial cues, a large number of graph-based MTSC approaches have been proposed, as the graph topology and edges can explicitly represent relationships among variables (channels), where not only various MTS graph representation learning strategies but also different Graph Neural Networks (GNNs) have been explored. Despite such progresses, there is no comprehensive study that fairly benchmarks and investigates the performances of existing widely-used graph representation learning strategies/GNN classifiers in the application of different MTSC tasks. In this paper, we present the first benchmark which systematically investigates the effectiveness of the widely-used three node feature definition strategies, four edge feature learning strategies and five GNN architecture, resulting in 60 different variants for graph-based MTSC. These variants are developed and evaluated with a standardized data pipeline and training/validation/testing strategy on 26 widely-used suspensor MTSC datasets. Our experiments highlight that node features significantly influence MTSC performance, while the visualization of edge features illustrates why adaptive edge learning outperforms other edge feature learning methods. The code of the proposed benchmark is publicly available at https://***/CVI-yangwn/*** Codes 68T10 © 2025, CC BY.

关键词： Graph neural networks

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High-Resolution Synthetic Aperture Imaging Algorithm for Terahertz 3

High-Resolution Synthetic Aperture Imaging Algorithm for Ter...

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3rd International Conference on Defence Technology, ICDT 2022

作者： Wang, Guanwen Liu, Zhaoyang School of Information Science and Engineering Shenyang University of Technology Shenyang110870 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang110169 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

The reconstruction and recovery of targets in the terahertz band has always been a direction for the improvement of synthetic aperture radar imaging algorithms. In order to improve the imaging resolution of the target, the scattering principle of electromagnetic waves to the target is analyzed. Through model establishment and formula derivation, the propagation process between the electromagnetic wave and the target is obtained, and the simulation and experiment verify that the corresponding algorithm can greatly improve the accuracy of target recovery. © Published under licence by IOP Publishing Ltd.

关键词： Electromagnetic waves

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Source-ID-Tracker: Source Face Identity Protection in Face Swapping

Source-ID-Tracker: Source Face Identity Protection in Face S...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Lin, Yuzhen Chen, Han Maiorana, Emanuele Campisi, Patrizio Li, Bin Shenzhen University Guangdong Key Lab of Intelligent Information Processing Shenzhen Key Lab of Media Security Shenzhen China Electronics and Mechanical Engineering Roma Tre University Department of Industrial Rome Italy Shenzhen Institute of Articial Intelligence and Robotics for Society Shenzhen China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Swapping faces with deep learning technology to generate realistic fake videos/images (a.k.a, deepfakes) has drawn great public con-cerns recently. Numerous approaches have been proposed to iden-tify fake contents;however, less work has been dedicated to pro-tecting the source faces in an active way. In this paper, we stand for a legitimate faceswap service provider and present an approach called Source-ID- Tracker (SIDT), which aims to protect the identity of source faces in deepfakes from malicious uses. As a plug-in, the encoder of SIDT implicitly embeds a source face image into a deep-fake image while ensuring the resultant encoded image is visually indistinguishable from the deepfake image. After sharing through social media, the embedded source face and its identity can still be recovered with a decoder. Experimental results show that the pro-posed model achieves a promising performance, in terms of reconstruction quality and attribution inference accuracy, in revealing the hidden source face. © 2022 IEEE.

关键词： Deep learning

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Complex-valued convolutional neural network for terahertz image classification 11

Complex-valued convolutional neural network for terahertz im...

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Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI 2022

作者： Hu, Nairui Qi, Feng Li, Yufeng Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Aerospace University Liaoning Shenyang110136 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9781510660939

Complex-valued convolutional neural network (CVCNN) have better performance than real-valued neural networks in the field of terahertz imaging. In this paper, a complex-valued neural network is innovatively applied to the terahertz image classification task in a vector network analyser (VNA) imaging system. The complex-valued CNN (CVCNN) processing framework for terahertz image classification is proposed. Terahertz image datasets are constructed using MINIST handwritten datasets and PSF which was measured from our transmission system. Compared to CNN, CVCNN has a better accuracy rate, and it is significantly less vulnerable to overfitting. Phase information can be used well at the same time, which is impossible for the CNN. The method of training data generation is given, and some specific implementation details are given. The superiority of the method in this paper is verified by using simulated and measured data obtained from a 200Ghz image system. © 2023 SPIE.

关键词： Image classification

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Accurate Segmentation of Lumbar Spine Magnetic Resonance Imaging by Deep Neural Network with Size-Adaptive Loss 23

Accurate Segmentation of Lumbar Spine Magnetic Resonance Ima...

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15th International Conference on Machine Learning and Computing, ICMLC 2023

作者： Zhang, Yingdi Shi, Zelin Wang, Huan Yan, Chongnan Wang, Lanbo Mu, Yueming Liu, Yunpeng Wu, Shuhang Liu, Tianci Pang, Mingqi Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Spine Surgery Department ShengJing Hospital of China Medical University Shenyang China

ISBN: (纸本)9781450398411

Accurate segmentation of lumbar spine is crucial for the assessment of spine disease diagnosis. The accuracy and robustness of segmentation is challenged by morphological complexity of lumbar spine anatomy structure, low contrast and background noise. Moreover, unbalanced anatomical structure impede the segmentation of small-size anatomy. To overcome these problems, an automatic segmentation method of lumber spine MRI is needed. In this paper, we propose to segment lumbar spine using a convolutional neural network (CNN) model. Based on the canonical u-shaped model, a multi-scale feature fusion module (MSFF) is proposed as bottleneck to better capture lumbar spine anatomy information of different sizes. To address the problem of anatomy size imbalance, we reshape the standard cross entropy loss as Size-Adaptive loss such that it adaptively weights the loss assigned to anatomy with different sizes. In order to fully verify the accuracy and effectiveness of the proposed method, we construct the first lumbar spine anatomy segmentation dataset which consists of 747 lumbar axial images from 83 patients. The dice similarity coefficient (DSC) is used to evaluate the segmentation methods. Compared with other methods, the proposed method achieves the best results with the highest average DSC of 0.8911. Ablation study shows that the proposed MSFF module and SizeAdaptive loss can promote segmentation accuracy a lot compared with the baseline model. The results indicate that our proposed method can overcome the influence of morphological variety in some degree and is competitive with other current medical image segmentation algorithms. This novel methodology will be expected to use in clinic for image diagnosis in the future. © 2023 ACM.

关键词： Magnetic resonance imaging

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Enhance Terahertz Image Resolution via Complex-valued convolutional neural networks 3

Enhance Terahertz Image Resolution via Complex-valued convol...

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3rd International Conference on Defence Technology, ICDT 2022

Terahertz imaging technology is widely used in nondestructive testing (NDT), human security inspection, far-field imaging systems, and so on. Improving image resolution is always a topic of discussion. For visible image and image super-resolution, the convolutional neural network (CNN) has achieved a better improvement effect than the traditional algorithm. Compared to optics, it is quite feasible to gain both the amplitude and phase information in THz imaging, the Complex-valued convolutional neural network (CV-CNN) is better suited for the field of terahertz imaging than the real-valued neural network. In this paper, A lightweight complex-valued neural network is constructed which enhances terahertz imaging in a vector network analyzer (VNA) imaging system. Compared to CNN, CV-CNN has a better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and it is significantly less vulnerable to overfitting. Phase information can be used well at the same time, which is impossible for CNN. The network is trained using the MNIST dataset and verified by using simulated and measured data obtained from a 200Ghz imager. © Published under licence by IOP Publishing Ltd.

关键词： Image resolution

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Multi-View Clustering Analysis via Density Peak Clustering with Novel Co-Training Strategy 17

Multi-View Clustering Analysis via Density Peak Clustering w...

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17th International Conference on Computational Intelligence and Security, CIS 2021

作者： Qiu, Zhenghao Jia, Hong College of Electronics and Information Engineering Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Media Security Shenzhen China

ISBN: (纸本)9781665494892

Multi-view clustering has emerged as an advanced technique for multi-view learning. In this paper, we propose a multi-view clustering algorithm based on density peak clustering. Combined with a novel co-training strategy, the proposed method adjusts the distances of intra-cluster and inter-cluster simultaneously by using the consistency and inconsistency of each view. Subsequently, the final clustering result is obtained by integrating the clustering result of each view while reducing the interference of low-value views. Experimental results on 8 real multi-view data sets validate that the proposed algorithm can make full use of the information of different views and obtain good clustering accuracy. © 2021 IEEE.

关键词： Clustering algorithms

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