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检索条件"任意字段=4th International Multi-Conference on Computing in the Global Information Technology"

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Domain Generalization in Myocardial Pathology Segmentation with MixUp Augmentation 1st

Domain Generalization in Myocardial Pathology Segmentation ...

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1st MICCAI Challenge Comprehensive Analysis and computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Chen, Leyang Tu, Yaosheng Bai, Penggang Pan, Lin College of Physics and Information Engineering Fuzhou University Fuzhou China Department of Radiation Oncology Fujian Cancer Hospital and Fujian Medical University Cancer Hospital Fujian Fuzhou China

ISBN: (纸本)9783031870088

MyoPS (Myocardial Pathology Segmentation) is used for auxiliary diagnosis of myocardial infarction by accurately segmenting myocardial lesions (such as scars and edema). However, CMR images are complex, manual segmentation is time-consuming and relies on professional knowledge, and there are differences in imaging data from different centers, which increases the difficulty of segmentation. To this end, this study developed a domain generalization module that flexibly integrates LGE, T2-weighted, and Cine sequences to improve cross-center and multi-sequence adaptability and robustness. Our method combines the domain generalization module with the nnUNet segmentation network, and reduces the differences between different data distributions by utilizing the domain generalization module for data mixing enhancement, thereby enhancing the model’s generalization ability and improving segmentation performance. In tests conducted on the data set of the MyoPS++ Challenge, our network performed well in segmenting scars and edema. Compared with the native segmentation network, it has a greater performance improvement, which verifies its ability to handle multi-center, Effectiveness in multi-sequence CMR data. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Real-Time Sports Image Recognition System Based on Deep Learning Algorithm 14th

Real-Time Sports Image Recognition System Based on Deep Lear...

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14th international conference on Frontier computing, FC 2024

作者： Zhang, Tai School of Economics Wuhan Donghu University Hubei Wuhan430212 China

ISBN: (纸本)9789819623907

Under the advocacy of "Healthy China", people pay more and more attention to sports. However, the traditional motion monitoring system is still limited in the aspect of posture information monitoring, especially in the aspects of limited capture range, data loss, and inaccurate motion recognition. Based on several Kinect devices, combined with Taijiquan and other sports, this paper explores a new way of sports posture monitoring. the research covers Kinect technology, bone tracking, data fusion, movement recognition and other fields. the system first collected human bone information under multiple Kinect, and extracted key bone point data through calibration and fusion technology, which was used as the basis for subsequent action recognition. the real-time classification and recognition of motion is successfully realized by using the extracted real-time bone coordinates and Angle features and the dynamic time warping algorithm. Aiming at the shortcomings of existing monitoring systems, this paper proposes a real-time recognition system of sports images based on multi-sensor fusion. through the joint deployment of angular velocity and inertial sensor, combined with the wavelet technology to process the data, the system has achieved a callback rate of more than 90% and a response within 0.51 s, which brings broad application prospects for sports posture information monitoring. the aim of this study is to provide effective monitoring and guidance for physical activity, reduce risks, optimize outcomes, and thus provide reliable support for healthy physical activity participants. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： information fusion

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Integrating Human Motion Dynamics in CNN Architecture to Recognize Human Activity from Different Camera Angles 4

Integrating Human Motion Dynamics in CNN Architecture to Rec...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Gupta, Kishan Kesari Lee, Joo-Ho Kaveri, Parag Ravikant Awasthi, Prashant College of Information Science and Engineering Ritsumeikan University Shiga525-0058 Japan Symbiosis Institute of Computer Studies and Research Symbiosis International University Pune411016 India

Human Activity Recognition (HAR) is a crucial component of computer vision, with applications in human-computer interaction and surveillance. As the need for HAR technology keeps increasing, so does the desire for solutions that can help people train by showcasing professional moves. For instance, new recruits can be successfully trained in particular fighting skills by observing the activities of seasoned soldiers. In order to increase the accuracy and dependability of HAR systems, this study investigates the incorporation of human motion dynamics into Convolutional Neural Network (CNN) architectures. this study enhances CNN's ability to capture both spatial and temporal features by incorporating dynamic changes in human movement as additional inputs, which results in a more complex comprehension of human activity. A significant identification of complex human activity and frequent movement is made viable by the architecture's proficiency in uniting motion data with classic graphic information. Experimentations operated on prominent datasets reveal that motion dynamics significantly enhance recognition exactness, mainly under challenging circumstances like occlusions, inconsistent viewpoints, and complicated actions. this study highlights how motion-informed CNN architectures can enhance HAR classification and open new avenues for multimodal action recognition research. © 2025 the Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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OCR4HSV: A multi-task Learning Approach for Handwritten Signature Verification 27th

OCR4HSV: A Multi-task Learning Approach for Handwritten Sig...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Lin, Chao-Qun Wang, Da-Han Su, Yan-Fei Ge, De-Wu Zhang, Xu-Yao School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China Xiamen KEYTOP Communication Technology Co. Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Handwritten signature verification (HSV) models are notably recognized for their ability to discern whether a signature is forged in an offline document. Recently, HSV technology has made significant development. However, these methods have primarily focused on the shape features of the text image and overlooked the textual feature information inherent in the text itself, which makes the HSV model overfit. In this paper, we propose a novel network model named OCR4HSV to solve the above shortcomings. the proposed OCR4HSV first attempts to combine OCR and HSV to learn textual features in a multi-task learning manner. the model employs a dual-parameter approach, combining shared parameters and independent parameters. Specifically, Within the shared parameters, the Laplace attention module (LAM) is incorporated for edge information extraction. For independent parameters, CN-Mamba is utilized for sequence feature extraction in OCR, and the multi-scale global fusion block (MGFB) is designed to enhance the distinction between reference and test sample pairs. Leveraging OCR-related information and these architectural enhancements can fully mine the inherent textual feature information and significantly mitigate overfitting in the HSV task, thereby boosting verification accuracy. Our model has achieved state-of-the-art performance on the ChiSig and HanSig datasets. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： multi-task learning

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Pipeline leakage location method based on 1DLKNet and spectrum weighting 4

Pipeline leakage location method based on 1DLKNet and spectr...

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4th international Computational Imaging conference, CITA 2024

作者： Gan, Weibing Wulamu, Ajiaikebaier Li, Xiaolong Zhang, Ziyue Cheng, Yining Xiaodan, Liang Yuanyuan, Li Zhang, Cui National Engineering Research Center of Fiber Optical Sensing Technology and Networks Wuhan University of Technology Wuhan430070 China School of Information Engineering Wuhan University of Technology Wuhan430070 China Chongqing Airport Group Limited Company Chongqing401120 China

ISBN: (纸本)9781510688834

With the advancement of the economy, urban areas are expanding in scale. Pipeline transportation, as the primary mode of gas conveyance, is increasingly extending into the subterranean regions of cities. Gas is an indispensable element of daily life, providing substantial convenience. However, safety hazards persist in the process of pipeline transportation. For instance, pipeline leaks, if detected promptly, may not pose significant issues. However, if undetected for prolonged periods, leaks can result in pipeline corrosion in mild cases, and in severe instances, even explosions, thereby posing considerable risks to public safety. Given the potential hazards associated with pipeline leaks, it is crucial to monitor the transportation status and structural integrity of pipelines. Numerous methods have been proposed by predecessors to monitor pipeline leaks, and currently, various techniques are employed for this purpose. this paper utilizes grating array sensing technology. the grating array is affixed in a linear configuration on the pipeline surface, and a compressor is used to pressurize the pipeline with air. A leakage column is introduced to simulate pipeline leaks under different pressures and apertures. the grating array collects vibration signals from pipeline leaks under these varying conditions. Upon capturing the leakage signals, the frequency generated by the pipeline leakage is effectively enhanced using the spectrum weighting method. A one-dimensional large kernel residual convolutional neural network is proposed, which not only captures the global information of the leakage but also detects the local characteristics. Ultimately, the proposed method demonstrates particular superiority in data processing. Experimental results confirm the efficacy of the method, these metrics underscore the method's robustness and reliability in detecting pipeline leaks. © 2025 SPIE.

关键词： Pipeline corrosion

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the Implementation of Indoor Navigation Using Augmented Reality to Enhance Users Experience (UX)

The Implementation of Indoor Navigation Using Augmented Real...

引用

computing and information technology (ICCIT-1441), 2020 international conference on

作者： Mashael M. Khayyat Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Nowadays, the global Positioning System (GPS) is used in outdoor environments as a navigation system, but there is still a need for an indoor navigation system. thus, this paper describes a proposed application that uses the augmented reality (AR) navigation system and creates a virtual character as guidance in a real-world view. the focus is on how the user can follow a virtual character to reach the desired indoor destination. the proposed method supports indoor and outdoor environments using a computer vision system to identify places. the application uses AR to render points of interest and navigation paths in the camera view. It has been found that AR provides a unique user interface for mobile applications that create human-computer interaction (HCI); therefore, smartphones are starting to embrace this HCI. the merge of AR, computer vision, and navigation technology will provide opportunities to adopt and develop new felids to create a new user experience. Navigation has been integrated with AR to enhance navigation usage. Besides that, adding a virtual character improved the user experience. As a result of using the proposed application, users reached their destinations interactively and enjoyably.

关键词： Human computer interaction Computer vision Indoor navigation Solids User experience Mobile applications information technology Augmented reality Smart phones global Positioning System

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multi-scale Local and global Context-aware Network for Speech Enhancement in Insect Noise Environments

Multi-scale Local and Global Context-aware Network for Speec...

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Advances in Electrical, Electronics and computing technology (EECT), international conference on

作者： Jian He Chao Zhang Guojing Li School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

In the summer and autumn seasons, insect chirping (such as cicadas) emerges as the predominant environmental noise interference in outdoor audio data collection. Although existing speech enhancement models have made progress, they often fail to fully utilize multi-scale information and ignore feature interactions between scales. To address these limitations, this paper introduces a multi-scale Local and global Context-aware Network (MLGNet) for solving insect noise interference. MLGNet is based on UNet with Local-global Scale-Aware (LGSA) module, aiming to learn a wider range of multi-scale contextual features. LGSA consists of three key components. Specifically, multi-scale global-based Attention (MGA) module aggregates multi-scale information from different channel levels for global feature modeling and encodes causal position information. the multi-scale Interactive Convolution (MIC) module achieves crossscale interaction modeling of local features through dual scale cross learning. Meanwhile, Context-aware Enhancement Fusion (CEF) module adaptivly learns of local and global feature weights and enhances nonlinearity, promoting high-quality dynamic fusion of features. the quantitative evaluation of the Insect Noise dataset and the Voice Bank+DEMAND dataset demonstrate that MLGNet outperformed other models and achieved excellent results in objective evaluation metrics.

关键词： Measurement Adaptation models Microwave integrated circuits Insects Aggregates Working environment noise Noise Interference Speech enhancement Data collection

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Genome Motif Discovery in Zika Virus: Computational Techniques and Validation Using Greedy Method 4

Genome Motif Discovery in Zika Virus: Computational Techniqu...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Mahapatro, Pushpa Susant Saini, Jatinderkumar R. Vaidya, Shraddha Vidyalankar School of Information Technology Wadala East Mumbai400037 India Pune412225 India

Identifying patterns in the genome sequences is an essential yet tricky task in Bioinformatics. It provides information about gene activity and gene functionality. In Deoxyribose Nucleic Acid (DNA) sequence analysis, computational approaches like Greedy motif search can be applied for motif identification. It finds recurring patterns called motifs by iteratively selecting the most promising sequence of a specified length from each DNA string. the selected string maximizes the scoring function and hence is selected. First, a set of initial motifs is selected for each set in the input string. then, a subsequence that best aligns with the selected string is selected for the next iteration. the score is calculated and needs to be minimized. the validation of the obtained motif is also performed. this study focuses on applying the algorithm to identify patterns in the genome sequence of the Zika virus. In finding conserved patterns in the Zika virus genome sequence, the Greedy motif search is known for its efficiency and precision. the Greedy motif search results are compared with Gibbs sampler method of motif identification. this study adds knowledge of the viral genome and suggests new treatment development methods by confining these patterns. © 2025 the Authors. Published by Elsevier B.V.

关键词： Genome

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19th international Symposium on Visual computing, ISVC 2024

19th International Symposium on Visual Computing, ISVC 2024

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19th international Symposium on Visual computing, ISVC 2024

ISBN: (纸本)9783031773914

the proceedings contain 73 papers. the special focus in this conference is on Visual computing. the topics include: AFIDAF: Alternating Fourier and Image Domain Adaptive Filters as an Efficient Alternative to Attention in ViTs;multi-Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay;improving Zero-Shot Template-Based 6D Pose Estimation with Geometric Features;contrastive Loss Based on Contextual Similarity for Image Classification;estimation of global Illumination Using Cycle-Consistent Adversarial Networks;anisotropic Point Synthesis by Example;3D Fluid Shape Control by Direct Manipulation;an Epithelium-Inspired Deformation Modeling Framework for 4D Sheets;hybrid Voxel Formats for Efficient Ray Tracing;PIEPredict++: An Improved Pedestrian Intention Estimation Model Incorporating Comprehensive Environment information;infant Video Interaction Recognition Using Monocular Depth Estimation;real-Time Predictor in Two-Players Fighting Game via Vision Transformer;explainable Action-Recognition Based Approach for Unsupervised Video Anomaly Detection;LORTSAR: Low-Rank Transformer for Skeleton-Based Action Recognition;Pedestrian Tracking Using Ankle-Level 2D-LiDAR Based on ByteTrack;Human Pose Estimation-Based ID Assignment Method in the Wild: A Real-Time Approach;RGB-T-UV multi-modal Object Tracking Based on Transformer Network;evaluating the Impact of Dehazing Algorithms on Visual Object Tracking Performance;MEM: Mask Enhancement Model for Video Object Segmentation;analysis Automation for High Explosive Breakout Symmetry;CLAP: Concave Linear APproximation for Quadratic Graph Matching;generalizing Neural Radiance Fields for Robust 6D Pose Estimation of Unseen Appearances;effectiveness of Residual Noise Based Methods for Single Image Based Morphing Attack Detection: A Comparative Study;black Box Adversarial Face Transformation Network;bi-Feature Selection Deep Learning-Based Techniques for Speech Emotion Recognition;activeConfusion: Efficient Cold-Start

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19th international Symposium on Visual computing, ISVC 2024

19th International Symposium on Visual Computing, ISVC 2024

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19th international Symposium on Visual computing, ISVC 2024

ISBN: (纸本)9783031773884

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