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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

共 2036 条记录，以下是91-100 订阅

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Generating MPI Scene Representation with Self-Attention Network 16

Generating MPI Scene Representation with Self-Attention Netw...

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16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Huo, Zhiyong Liu, Wenjing Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Nanjing China

ISBN: (纸本)9798350330755

Multi-plane image (MPI) plays a crucial role in enabling efficient and unambiguous scene representation for view synthesis. Existing methods often suffer from artifacts and truncation issues in rendered views due to limited understanding of depth plane dependencies and semantics. We propose a Transformer-based algorithm for the MPI scene representation generation. Leveraging the self-attention, our approach captures global context, effectively modeling relationships between planes. We introduce a rendering method with stricter constraints for improved accuracy in predicting occluded regions. Experimental results demonstrate our method's superiority, with a significant 9.86% improvement in Structural Similarity Index (SSIM) for synthesizing novel views. Perceptual image patch similarity (LPIPS) decreases remarkably by 53.54%, enhancing perceptual quality, while peak signal-to-noise ratio (PSNR) improves by 34.98%. These results validate our method's effectiveness and high-quality MPI scene representation. © 2023 IEEE.

关键词： Semantics

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Brain-controlled Robotic Arm System based on Improved FBCCA Algorithm with Dynamic Window 17

Brain-controlled Robotic Arm System based on Improved FBCCA ...

引用

17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Han, Chunxiao Zhang, Jingyan Jia, Yuyu Che, Yanqiu Tianjin Key Laboratory of Information Sensing & Intelligent Control China School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin 300222 China

ISBN: (纸本)9798331507398

Brain-controlled system has been used in assisting disabilities, and how to enable such groups to have the ability to communicate with the surroundings has become the focus of research. However, due to the individual differences and the dynamic time-varying brain states, the fixed time window used in the traditional Filter Bank Canonical Correlation Analysis (FBCCA) algorithm for Steady-State Visual Evoked Potential-based Brain-Computer Interface (SSVEP-BCI) is not optimal, which limits its generalizability. Therefore, in this paper, based on the SSVEP experimental paradigm, we propose an improved FBCCA algorithm with dynamic window based on the difference level between correlation coefficients. Compared with the traditional FBCCA algorithm, the accuracy is significantly improved. Furthermore, this paper designs a BCI-based robotic arm control system, achieving real-time and precise object grasping. This validates the feasibility and effectiveness of the proposed algorithm. Our findings may provide a theoretical basis and technical support for the practical application of BCI. © 2024 IEEE.

关键词： Robotic arms

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Accelerated Robustness Certification for Diffusion Denoised Smoothing Based on image Super-Resolution 17

Accelerated Robustness Certification for Diffusion Denoised ...

引用

17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Zeng, Qian Peng, Anjie Deng, Fei Zeng, Hui School of Computer Science and Technology Southwest University of Science and Technology Mianyang China China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9798331507398

While diffusion denoised smoothing (DDS) achieves excellent certified defense performance under l2 norm bounded perturbations, we find that DDS is vulnerable to image up-sampling. The bicubic interpolation used in DDS reduces the classification accuracy of the base classifier, thereby weakening its certified robustness. As an alternative, we introduce image super-resolution technology to compensate for the loss of detail or unrealistic content generated by interpolated images, thereby improving the classification accuracy while adapting to the input size required by classifier, but with some additional time cost. Therefore, in this paper, we propose an accelerated certified defense for enhancing DDS based on image super-resolution. By adaptively reducing the certified sample size according to input features to achieve cost-effectiveness for robustness certification, and as the base classifier's predictions are more accurate on super-resolution images than on interpolated images, our proposed method can quickly and effectively enhance certified defense performance based on DDS. Experimental results show that we achieve a top-1 certified accuracy of 56.2% on imageNet under l2 bounded perturbation δ=1.0, whiche is at least 4% higher than the previous diffusion denoised smoothing, and saves nearly 1/3 of the total certified time compared to before. © 2024 IEEE.

关键词： Deep neural networks

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Spatial Characteristics Analysis and Visualization for Brain Networks of Motor imagery Based on Graph Convolutional Neural Networks 17

Spatial Characteristics Analysis and Visualization for Brain...

引用

17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Pan, Yuhang Xie, Li Wang, Daming College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China Zhejiang University School of Medicine First Affiliated Hospital Hangzhou China

ISBN: (纸本)9798331507398

As the most complex organ in the human body, the study of the human brain has always been a hot topic in the field of neuroscience. This paper mainly studies the relevant analysis and visualization of the spatial characteristics of the brain networks on motor imagery. In view of the fact that graph convolutional neural networks can better process graph structure data in non-Euclidean space and will not lose the coupling information between EEG signal channels, they are chosen as the model structure. We also study different graph convolutional neural network structures, and find the model structure with the best classification effect by adjusting the network structure and comparing the effects of different models. In addition, we evaluated the optimal training model in many aspects. Finally, in order to observe the spatial characteristics of EEG signals more conveniently, comprehensively and from multiple levels, we designed a functional and user-friendly visualization software based on the results, which displays the diverse information of raw EEG signals and brain networks. © 2024 IEEE.

关键词： Convolutional neural networks

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A Deep Residual Network-Based Model for image Steganography Analysis 17

A Deep Residual Network-Based Model for Image Steganography ...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： He, Fei Qin, Renchao Tan, Keqiang Zhang, Lan Long, Jianlin Duan, Jialin Li, Suonan School of Computer Science and Technology Southwest University of Science and Technology Sichuan Mianyang621010 China Sichuan ShuTian Information Technology Co. Ltd. Sichuan Yaan625015 China

ISBN: (纸本)9798331507398

Steganalysis is the process of detecting and analyzing steganography, which is widely utilized in fields such as network security, copyright protection, military, and intelligence. To address the challenges of existing steganalysis models, particularly their difficulty in capturing subtle steganographic information and the low detection rates for adaptive steganography algorithms, this paper proposes an image steganalysis model based on deep residual networks. In the preprocessing stage, the model captures residual features by combining fixed high-pass filters with learnable filters. During feature extraction, feature enhancement is performed between two sets of depthwise separable convolutions to improve the signal-to-noise ratio of the steganographic image. Four improved residual blocks are employed as the backbone network, with the inclusion of the ECA attention mechanism to further enhance the accuracy of steganalysis. The model was tested on the BOSSBase-v1.01 and BOWS2 datasets. Experimental results demonstrate that the model exhibits strong detection performance across different embedding rates of various steganography algorithms, particularly in the detection of the WOW algorithm. For the merged dataset of BOSSBase-v1.01 and BOWS2 with an embedding rate of 0.4bpp, the detection rate reached 95.0%, verifying the effectiveness of the proposed model. © 2024 IEEE.

关键词： Convolutional neural networks

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Pilots' Task Identification Using Visual Attention Distribution 17

Pilots' Task Identification Using Visual Attention Distribut...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Liao, Yuan Lu, Yanyu Zhang, Han Zhu, Erqin Fu, Shan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China China Southern Airlines Company Limited China

ISBN: (纸本)9798331507398

Pilots have different visual attention distributions when executing different tasks outlined in the Quick Reference Handbook (QRH). In this study, we use pilots' visual attention distribution to analyze their process of executing QRH tasks. We sampled the pilots' eye movement data by using eye-tracking devices and used a Markov Process model to extract features related to changes of visual attention distribution. A sliding window method was then used to describe the pilots' task switch process, and to identify the time point when pilots enter the procedure of a QRH task. We proposed the Adaptive Window Size (AWS) algorithm to overcome the limitations of the traditional sliding window method. The AWS algorithm dynamically adjusts the window size to fit the duration of each QRH task, enabling more accurate predictions of tasks. The results show that this model can accurately describe the whole process of pilots' QRH task execution and locate the time point when pilots enter the procedure of a QRH task. The meaning of this work is to provide a method to analyze the task execution process more precisely, and it is important for improving crew training, accident prevention, and flight safety. © 2024 IEEE.

关键词： Markov processes

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The Reliability of Satellite Precipitation Measurements in Singapore 17

The Reliability of Satellite Precipitation Measurements in S...

引用

17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Saharia, Dhiraj Lee, Yee Hui Dev, Soumyabrata Tezpur University Assam Tezpur India Nanyang Technological University Singapore Singapore The ADAPT SFI Research Centre Dublin Ireland School of Computer Science University College Dublin Dublin Ireland Beijing-Dublin International College Beijing China

ISBN: (纸本)9798331507398

The study and analysis of precipitation on the surface of the earth is important for understanding the hydrological cycle of the planet. Estimating precipitation variations across space and time has widespread applications in environmental monitoring, issuing drought warnings, and increasing agricultural productivity. In this paper, we quantify the reliability of satellite precipitation measurements compared to ground-based measurements using Singapore as a case study. We test the strength of the correlation and also the similarity between different satellite data sources with respect to our private and high resolution weather station. We obtain high degree of correlation between satellite- and ground- based measurements indicating the potential to provide reliable precipitation estimates. This is immensely useful as satellite data prove to offer us fantastic alternatives as compared to the point measurements from ground-based precipitation measurements. © 2024 IEEE.

关键词： Weather satellites

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ARMA Graph Filter Design by Least Squares Method Using Reciprocal Polynomial and Second-order Factorization 16

ARMA Graph Filter Design by Least Squares Method Using Recip...

引用

16th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2023

作者： Xu, Luming Jiang, Aimin Li, Min Tang, Yibin Zhu, Yanping Hohai University College of Information Science and Engineering China Changzhou University School of Microelectronics and Control Engineering China

ISBN: (纸本)9798350330755

Graph filtering plays a pivotal role in the domain of graph signal processing. In this paper, we propose a novel algorithm for designing autoregressive moving average (ARMA) graph filters. Our method is built upon classical least squares (LS) techniques. A reciprocal polynomial is introduced, making the resulting problem more tractable. To address stability concerns, a second-order factorization of the denominator polynomial is further incorporated. We develop an alternating optimization approach to tackle the design problem, where numerator coefficients, coefficients of the denominator's second-order factors (SOCs), and those of its reciprocal polynomial are updated alternately. Simulation results demonstrate that our proposed algorithm outperforms existing graph filter design methods in terms of design accuracy. © 2023 IEEE.

关键词： Factorization

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A Study on an Improved ResNet Model for image Classification 17

A Study on an Improved ResNet Model for Image Classification

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Liu, Yi Wang, Weihu He, Wenbin School of Computer and Information Science HuBei Engineering University XiaoGan China College of Technology HuBei Engineering University XiaoGan China

ISBN: (纸本)9798331507398

ResNet effectively addresses the vanishing gradient problem in deep networks by introducing skip connections, making it a leading model in image classification tasks. This paper proposes an improved ResNet-based model leveraging transfer learning from imageNet, initially incorporating additional layers such as Flatten, fully connected layers, L2 regularization, Batch Normalization, and Dropout. The research began with an 'initial improved version' of ResNet50, designed to enhance stability and generalization. Subsequent experiments focused on further optimizations inspired by recent studies, particularly the strategic placement of Dropout layers. Comparative experiments on the CIFAR-10 dataset included the original ResNet50, transfer learning-enhanced ResNet, and multiple improved variants. The results demonstrate that the initial improved model significantly enhances stability and generalization compared to the original ResNet. Further optimizations, such as placing Dropout before the Global Average Pooling (GAP) layer, yielded the best performance on the training set, while placing Dropout after the Flatten layer and before the Dense (128) layer resulted in superior validation performance. This study underscores the benefits of a stepwise optimization approach, combining transfer learning, structural modifications, and strategic Dropout placement to enhance image classification performance. © 2024 IEEE.

关键词： Federated learning

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PCA Dimensionality Reduction Algorithm Based on Ordered Pair of Normalized Real Numbers 17

PCA Dimensionality Reduction Algorithm Based on Ordered Pair...

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17th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2024

作者： Zhao, Xiang Zheng, Yi Pan, Xiaoqin Zhou, Lei School of Computer Science and Technology Southwest University of Science and Technology China Engineering Technology Center Southwest University of Science and Technology China School of Mathematics and Physics Southwest University of Science and Technology Sichuan621010 China

ISBN: (纸本)9798331507398

This paper presents a PCA (Principal Component Analysis) data dimensionality reduction algorithm based on OPNs (Ordered Pair of Normalized Real Numbers), referred to as OPNs-PCA. This algorithm aims to improve the dimensionality reduction performance of data. Especially, it shows significant advantages in removing feature redundancy and noise, providing a clearer and more effective data basis for subsequent machine learning or data mining tasks. The algorithm proposed in this paper introduces the concept of OPNs into the PCA algorithm and performs PCA dimensionality reduction operations under the framework of OPNs. Through the characteristic of OPNs' operation in processing feature relationships in non-real number spaces, it can effectively reduce data noise, enhance the processing ability of nonlinear data, and improve the performance of subsequent classification algorithms. We have conducted detailed experimental verification on multiple public data sets. The results show that unlike classic PCA, LLE, KPCA, SPCA and other algorithms that require more principal components to retain sufficient information, OPNs-PCA can achieve a similar or even better level of information retention with fewer principal components, and has a significant improvement in the processing ability of nonlinear data. © 2024 IEEE.

关键词： Dimensionality reduction

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