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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Intelligent processing algorithms for Motion Capture Data Oriented to Artificial Intelligence 2

Intelligent Processing Algorithms for Motion Capture Data Or...

引用

2nd IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Lu, Zhenglei Beijing Information Technology College General Ability Teaching Department Physical Education Teaching Research Office Beijing China

ISBN: (纸本)9798350317558

Traditional motion capture systems are prone to environmental interference, resulting in noise and errors in the captured data. This article proposes an artificial intelligence oriented intelligent processing algorithm for motion capture data, which improves the quality of motion capture data and reduces noise and errors. This article utilizes sensors to collect a large amount of motion timing data and motion image data, preprocesses the data, and constructs a Convolutional Neural Network (CNN) Long Short Term Memory (LSTM) model. By using the CNN model to extract features from images, this paper uses the LSTM model to analyze temporal data, fuses the output of LSTM and CNN, and finally estimates the motion attitude. The data results of the test set indicate that the average mean square error of motion attitude estimation using the CNN-LSTM model is only 0.03. The use of the CNN-LSTM model can effectively improve the performance of motion data pose analysis. © 2024 IEEE.

关键词： attitude estimation convolutional neural network intelligent processing long short term memory motion capture data

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Fatigue Driving Detection Based on Facial Multi-Information Feature Fusion 7

Fatigue Driving Detection Based on Facial Multi-Information ...

引用

7th International conference on Computer Network, Electronic and Automation, ICCNEA 2024

作者： Qu, Wenjing Wang, Zhongsheng School of Computer Science and Engineering Xi'an Technological University Xi'an China

Driver fatigue driving risk detection is one of the important areas of road traffic safety research in China. According to statistics, more than 40% of China's traffic accidents every year are related to driver fatigue. In order to reduce this risk, this paper proposes a new method based on facial multi-information feature fusion to realize real-time fatigue driving detection. The method includes using the camera to capture the driver's face image in real time, combining the face recognition in Dlib library and the detection of 68 key points, and implementing the face detection through OpenCV. Further, the opening and closing states of eyes and mouth were analyzed based on EAR and MAR algorithms, and a reasonable threshold was set to judge fatigue behavior. The HPE head pose estimation method is used to judge the fatigue state by considering the pitch Angle and roll Angle information. Finally, the improved rough set theory comprehensive evaluation method is used to carry out decision fusion processing on the feature results, and intuitively display the system detection results, which is convenient to evaluate the fatigue state of the current driver. The experimental results show that the multi-feature fusion detection method has good real-time performance, the accuracy rate is 94.55%, and the fatigue state can be detected quickly and accurately under various driving attitudes. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Face recognition

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MobileNet-V3-based sea-land segmentation for maritime search radars 15

MobileNet-V3-based sea-land segmentation for maritime search...

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15th International conference on Signal processing systems, ICSPS 2023

作者： Xue, Jian Ma, Manshan School of Communications and Information Engineering Xi’an University of Posts and Telecommunications China

ISBN: (纸本)9781510675056

The effective perception of marine detection scenarios is essential for maritime search pulse radar to detect and track maritime targets. However, the dynamically changing and complex marine environment makes it challenging for maritime search pulse radar to accurately perceive the sea and land regions. To improve the accuracy of sea-land region perception, and reduce the computation and complexity of algorithm of the network, this paper proposes a MobileNetV3-based maritime search pulse radar sea-land segmentation method. Firstly, a radar sea-land segmentation dataset based on PPI (Plan Position Indicator) is constructed using real measured data from different scenarios and sea conditions. Subsequently, the MobileNetV3 network is trained on this dataset to achieve sea-land segmentation for maritime search pulse radar. Experimental results demonstrate that the segmentation accuracy of the MobileNetV3-Lbased radar sea-land segmentation method surpasses its competitors. © 2024 SPIE.

关键词： image segmentation

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Real Time image processing and Operation Management of Multicultural Communication Platforms Based on Deep Learning

Real Time Image Processing and Operation Management of Multi...

引用

2023 International conference on Mechatronics and Intelligent Control, ICMIC 2023

作者： Zhang, Chi Guo, Jiaqi Huanghe S&T University Jiyuan454650 China International College Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9781510668393

The promotion and utilization of modern technology has made intercultural exchanges more frequent and deep, andsecure image encryption and processing are hot issues for multicultural IOT communication platforms in the context ofglobalization for smart cities. In this paper, the re-al-time image encryption test and resource scheduling optimizationanalysis of multicultural communication platform are carried out based on convolutional neural network. First, weencrypt the image based on the layered image encryption technology of the chaotic system and the Feistel network. Secondly, through different strength encryption algorithms and different convolutional network structures, the encryptedimages are classified and analyzed, and the encryption effect of the images is tested by the classification accuracy. Inaddition, we construct a task-resource scheduling model and design scheduling optimization schemes for the model fromthe aspects of data flow and control flow, so as to realize the mapping of network model to the IOT cloud plat-formandthe reasonable scheduling of task resources on the software and hardware side. © 2023 SPIE.

关键词： Deep learning

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Application Research of Pointer Instrument Panel Extraction Algorithm Based on Deep Learning 8

Application Research of Pointer Instrument Panel Extraction ...

引用

8th International conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Zhang, xianwei He, Shanlian Xi'an Shiyou University School of Computer Science Shaanxi Xi'an China

ISBN: (纸本)9798350302455

Pointer-type meters can suffer from inefficiencies in using manual data reading due to the lack of digital interfaces. Traditional pointer gauge reading recognition algorithms can only operate in specific environments or fixed locations without high reliability. In this paper, by introducing the traditional dial extraction algorithm Hoff circle detection, we propose the improved Mask R-CNN deep learning algorithm for automatic recognition of pointer-type dashboard based on Mask R-CNN algorithm, and introduce the maximum pooling algorithm for image feature extraction and the improved method of using PrROIPooling pooling technique. Experiments show that the improved Mask R-CNN algorithm improves the target detection accuracy by 2.1% and the instance segmentation accuracy by 1.9%. Compared with the traditional dial localization algorithm, this algorithm has the features of hih accuracy and robustness. © 2023 IEEE.

关键词： Instrument panels

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Positional Feature Generator-Based Transformer for image Captioning 18

Positional Feature Generator-Based Transformer for Image Cap...

引用

18th International conference on Intelligent systems and Knowledge Engineering, ISKE 2023

作者： He, Shuai Yang, xiaobao Ma, Sugang Song, Bohui He, Ziqing Luo, Wei School of Computer Science and Technology Xi'An University of Posts and Telecommunications Xi'an China School of Computer Science and Engineering Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350318401

The Transformer-based architecture achieves state-of-the-art results in image captioning. Due to its non-recurrent nature, additional positional information needs to be provided. However, existing advanced methods attach positional information to the model by additional encoding or embedding, which is independently decoupled from the original input features. In addition, no matter absolute or relative methods, the encodings are fused with input features by add operation, which leads to information interference between the two types of features and affects the performance of the model. In this paper, we propose a novel architecture to remedy the above limitations, called positional feature generator (PFG). This module is effective in modeling image spatial positional frame by graph structure, which can learn absolute position explicitly and relative position implicitly. Meanwhile, we concatenate the captured positional features with the original features, making the positional information as a separate additional feature to avoid feature interference. Extensive experiments on MS COCO validate the effectiveness of PFG. Moreover, PFG outperforms some state-of-the-art positional representation methods, and positional feature generator-based Transformer (PFGT) is competitive with some state-of-the-art image captioning algorithms. © 2023 IEEE.

关键词： Encoding (symbols)

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Adaptive Feature Calibration Siamese Network for Rotating Object Tracking 2

Adaptive Feature Calibration Siamese Network for Rotating Ob...

引用

2nd International conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Zhu, Zhiqiang Sun, Jiwei Li, Ziang Tian, Gao Xidian University School of Electronic Engineering Xi'an China

ISBN: (纸本)9798350331417

The Siamese-based object tracking algorithms employ random angle embedding to mitigate accuracy degradation caused by object rotation. However, it only adds prior knowledge as predefined patterns without alleviating the feature misalignment and local equivariance caused by rotation. In this paper, we propose a Siamese network with feature-calibration (AFC-Siam Net), which establishes the spatial correspondence relationship of the misalignment features for the same object under different rotation angles by dual-templates input structure with an affine transformation filter. Compared to existing Siamese-based trackers, AFC-Siam Net calibrates the features without the need for carefully designed prior knowledge. The experimental results confirm the effectiveness of the proposed algorithm through comprehensive evaluations on widely adopted datasets in the field of visual object tracking. © 2023 IEEE.

关键词： Feature Calibration Rotation Equivariance Rotation Object Tracking Siamese Networks

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IRT and ANN Based System Identification Schemes for Model Based Fault Detection of Nonlinear systems

IRT and ANN Based System Identification Schemes for Model Ba...

引用

IEEE International conference on Aerospace Electronics and Remote Sensing Technology (ICARES)

作者： Alwagait, Mohammed Khan, Mohammad Obaidullah Imam Mohammad Ibn Saud Islamic Univ Coll Engn Elect Engn Dept Riyadh Saudi Arabia

ISBN: (纸本)9798350344004

Faults in electrical power transmission systems can cause system failures and even may cause explosions. It is desired to remove a faulty component immediately to prevent further damage to the system and the environment. To address this, a typical technique is devised based on thermal imaging and various artificial neural network algorithms. The faulty element or component will radiate or emit higher energy when compared to normal or healthy conditions, because of the higher current flow rates. The thermal image taken from such defective part of the power system will be more highlighted in the image in contrast with the normal cool background. This drastic change in the grey level values in contrast with the healthy power system's picture hints or predicts a fault in that region. To support this methodology, an exhaustive simulation is implemented and demonstrated using thermal image processing and self-learning neural network algorithms and the simulation results are compared. This analysis is performed through various types of ANN techniques, and comparisons are established between them to report the network with best prediction results on a typical 'Step' dataset and 'Realistic' dataset. A similar low score mean square error MSE is exhibited with these models, and the R-square values are closer to the best score of one in all algorithms discussed. A better graph is obtained in Levenberg-Marquadrdt training Algorithm where most of the predictions fall alongside the target;however, Bayesian regularization gives a better plot than LM with the best fit is being obtained at lesser number of iterations that is in less time.

关键词： AI Artificial Neural Network Training algorithms image Extraction IRT Power System Faults Thermal Imaging

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Latest Trends on Satellite image Segmentation 5th

Latest Trends on Satellite Image Segmentation

引用

5th International conference on Communication and Intelligent systems, ICCIS 2023

作者： Borkar, Sahil Chidrawar, Krishna Naik, Sakshi Turuk, Mousami P. Vaijapurkar, Vaibhav B. SCTRs Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789819720781

For the last few decades satellite imaging technology has taken massive strides towards higher spatial resolution, larger swath coverage and almost real-time data delivery. Satellite imaging or remote sensing is extensively used in optical imaging of the globe, military surveillance, deforestation detection, etc. Multispectral imaging by satellite has enabled an exceptional understanding of the earth by imaging beyond the realm of the visible spectrum. Against this backdrop image segmentation using machine learning, deep learning models and image processing has prompted several new approaches and techniques for satellite image segmentation. This survey provides a comprehensive review of the recent literature, covering the novel approaches in image segmentation using satellite imaging as well as others. There is a broad coverage of segmentation algorithms of UNET, TSVM and Random Walker. It investigates the strengths, challenges and novel aspects and compares precisions and deliberate potential research outlooks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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AI-Based Feedback Model in Supporting Mobile-Assisted Language Learning Environment 1

AI-Based Feedback Model in Supporting Mobile-Assisted Langua...

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1st International conference on Software, systems and Information Technology, SSITCON 2024

作者： Chen, Jingchun Xi'an Fanyi University School of English Language & Culture Shaanxi Xi'an China

ISBN: (纸本)9798350352931

The integration of Artificial Intelligence (AI) in Mobile-Assisted Language Learning (MALL) environments has revealed potential for enhancing learner writing engagement. However, research conducted on the impact of AI-based feedback models within MALL context is meager. This study investigates the effect of an AI-based writing feedback model. We constructed an AI writing feedback model by combining advanced machine learning algorithms with natural language processing techniques. 95 intermediate Chinese learners were divided into control and experimental groups. The experimental group utilized a mobile platform equipped with the AI writing feedback model. Qualitative data like learners' writing samples were collected. The findings revealed a significant increase in learner writing engagement. Learners' participation and assignment completion rates all enhanced. Findings indicate learners make more informed revisions and have a deeper understanding of grammatical structures and vocabulary usage. Furthermore, the model has the ability to analyze and highlight common errors to identify learners' struggled areas. © 2024 IEEE.

关键词： Federated learning

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