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Using EEG Data to Classify Hand Motor imagery in Stroke Patients 4

Using EEG Data to Classify Hand Motor Imagery in Stroke Pati...

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4th international conference on Electronic Information Engineering and computer Technology, EIECT 2024

作者： Shen, Yiwei Liu, Junting Yang, Yuxuan Xie, Wenjun Hefei University of Technology School of Software Anhui Hefei China

ISBN: (纸本)9798331528850

EEG-driven brain-controlled prosthetics can greatly help people with movement disorders in their daily lives. Still, the task of accurately translating intentions into expected actions in the brain remains a technical challenge. In recent years, as a branch of machine learning, Deep Neural Networks (DNN) have made great progress in improving the recognition and classification ability of computer vision tasks. this study evaluate the information representation ability of 2 machine learning models and 16 NN models and their variants. the experiment is conducted on an open source EEG dataset of hemiplegic stroke patients, and we evaluate the thematic and cross-thematic performance of the above algorithm. We find that a single-layer GRU network remained an optimal choice in subject subject classification because it is able to effectively reduce model overfitting. For cross-subject analysis, some deep-learning models outperform all other methods due to the availability of training data and can function effectively in cross-subject decoding tasks. Independent Component Analysis (ICA) processing is also proved to be effective for improving the accuracy of classification. these improvements will provide important information for researchers in the field of Brain-computer interface (BCI) to select data processing strategies appropriate for their research tasks, it also lays a foundation for the future research on the interaction between the characteristics of bioelectric signals and the structure of neural networks. © 2024 IEEE.

关键词： Brain computer interface

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Real-Time Human Pose Estimation Using Media-Pipe an Artificial Intelligence Applications in Health and Fitness 4

Real-Time Human Pose Estimation Using Media-Pipe an Artifici...

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4th international conference on Artificial Intelligence and Signal processing

作者： Totlani, Ketan Dhavala, Shiva S. Vijayarao, S. Sandeep Kumar Challagundla, Yagnesh Roy, B. Zhuo, Eugenia R. Jaipur Natl Univ Sch Comp & Syst Sci Jaipur Rajasthan India Rashtreeya Vidyalaya Coll Engn Sch Informat Sci & Engn Bengaluru India RMK Engn Coll Dept Comp Sci & Engn Kavaraipettai Tamil Nadu India VIT AP Univ Sch Elect Engn Amaravati Andhra Pradesh India Univ Santo Tomas Manila Coll Informat & Comp Sci Manila Philippines

ISBN: (纸本)9798350350661;9798350350654

Significant progress has been achieved in the fields of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in recent years. Nevertheless, a significant gap continues to exist in the effective surveillance of human actions, activities, and the recognition of human movements or poses. Machines and computers are fundamentally incapable of comprehending human activities without user intervention. Consequently, we now depend on technology to record and archive human actions through live broadcast data, videos, or images. the field of pose estimation, which is a critical area of research with applications in fields such as Augmented Reality, Animation, Gaming, Healthcare, and Sports, is expanding due to the rapid advancement of computer vision technology. this project implements innovative methods to train models and determine poses, utilizing random inputs from internet images, videos, and live streaming. In particular, models that have been trained with artificial intelligence have been created for daily physical activities that necessitate intricate pose estimation. the initial step in the project is to utilize MediaPipe to estimate the poses of the human body. Subsequently, the angles between the body joints are calculated to create the desired models. Additionally, a web-based user interface (WEB-UI) was developed to enable access from any location. the methodology involves the extraction of data from critical body locations, the framing of models, and the utilization of the MediaPipe model due to its superior performance and features in comparison to other models. the objective of this method is to develop a web application for end-users, train AI models for daily physical activities, and establish an efficient system for single human pose estimation. this approach provides a cost-effective alternative to physical trainers and enables the monitoring of health and sports activities to be enhanced.

关键词： Human Pose Estimation Artificial Intelligence Machine Learning computer vision Deep Learning MediaPipe Physical Activity Monitoring Web Application Development

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RAG Beyond Text: Enhancing image Retrieval in RAG Systems 4

RAG Beyond Text: Enhancing Image Retrieval in RAG Systems

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4th IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Bag, Sukanya Gupta, Ayushman Kaushik, Rajat Jain, Chirag Data Science and Insights Genpact India Private Limited Bengaluru India

ISBN: (纸本)9798350395914

this paper presents a novel methodology for the extraction and retrieval of images in RAG (Retrieval Augmented Generation) powered Question Answering Conversational Systems that circumvents the limitations of Optical Character recognition and Large Language Model (OCR-LLM) powered traditional image retrieval approaches. We are leveraging the positional information of images in a vast array of multi-modal (text/image) documents for ingesting image information alongside text, followed by advanced retrieval and prompt engineering techniques to develop an RAG system that maintains the integrity of textual and visual data correlation in responses to queries pertaining to both text and images in QnA solutions and is adept at retrieving both OCR-compatible and OCR-incompatible images. We have successfully incorporated this approach over a variety of multimodal documents ranging from research papers, application documentations, surveys to guides and manuals containing text, images and even tables with images and managed to achieve SoTA (State of the Art) performance over simple to complex queries asked on the mentioned documents. Furthermore, our approach performed explicitly better in cases where vision Models like GPT-4 vision fails to accurately retrieve images which are OCR incompatible and pertains to highly customized scientific devices or diagrams and in cases where the image's visual representation is not semantically aligned with textual information but is important to be retrieved for completeness in the response. © 2024 IEEE.

关键词： Semantics

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Edge-Guided Human Depth image Inpainting Based on Diffusion Models 4

Edge-Guided Human Depth Image Inpainting Based on Diffusion ...

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4th IEEE international conference on Data Science and computer Application, ICDSCA 2024

作者： Xia, Shiji Du, Yi Zhang, Jingyi Yang, Zuopeng Wang, Yu Fu, Yue Liu, Ming Ding, Xie School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Data Intelligence Product Department Wonders Information Co. Ltd Shanghai China

ISBN: (纸本)9798350368239

In the context of depth images, fusing human segmentation images with depth background images provides critical data for human detection tasks, where accurate localization of the human head is a key indicator. However, in real-world scenarios with occlusions and complex backgrounds, human segmentation images often lack head pixels. this missing data leads to subpar performance in human recognition tasks for deep learning models. To address this issue, this paper proposes an efficient approach that combines edge detection, projection analysis, and template matching for upper-body localization and head identification. Additionally, a refined diffusion model is used to repair the human head edge mask obtained from the initial processing. the main advantage of this approach is its independence from complex deep learning model training. It integrates traditional image processing techniques, morphological analysis, and diffusion models, simplifying head localization and effectively avoiding interference from the shoulder and arm regions. this approach enables more accurate head localization and uses diffusion models to restore missing portions of the human head, thereby improving overall pose recognition accuracy. Experimental results demonstrate that the repaired depth images outperform the original images in human pose model training. When trained with the repaired images, detection accuracy (mAPVal50-95) and precision improved by 10.1% and 7.1%, respectively, significantly enhancing downstream object detection performance. these findings validate the effectiveness of the proposed method in repairing missing human head depth images. they further highlight its potential in improving pose recognition and object detection performance, offering reliable technical support for related tasks. ©2024 IEEE.

关键词： Template matching

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the last two decades of Studies in diffusion models: A bibliometric analysis 4

The last two decades of Studies in diffusion models: A bibli...

引用

4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Wang, Yuanyuan Li, Xiangjun Massey College Nanjing University of Finance and Economics Jiangsu Nanjing China

ISBN: (纸本)9798350326444

Given the importance of diffusion models in daily life and economic growth, developing efficient and accurate diffusion models is an important task. For example, the combination of diffusion models with computer vision is a specific area of studying diffusion models to meet social needs. Many activities within this field have benefited from recent technological advances. the diffusion model is inspired by the theory of nonequilibrium thermodynamics. First, according to the Markov chain definition, random noise will be added slowly in, and then the model has to learn the reverse diffusion process to construct the desired data sample from the noise. the research of diffusion model is the result of the multi-disciplinary integration, and it is also the practical development of the application scope, practicability and efficiency of text generation picture technology.[1] the application of diffusion model can promote the development of a number of technologies in this field, including computer vision, speech generation, bioinformatics, natural language processing, NLP, waveform signal processing, multi-modal modeling, molecular map modeling, time series modeling, antagonistic purification and other fields. the research content of the diffusion model is more cutting-edge, and its wide application is more in line with the needs of the future society. However, as a cutting-edge technology, the research of diffusion model is not perfect, and there are still problems such as slow sampling speed, poor maximum likelihood, weak data generalization ability, single data processing type, and model dimension reduction. Because this review comprehensively analyzes the prints and publications derived from web of science, based on the author, keywords, age and nationality, summarized the important trends and valuable insights. © 2023 IEEE.

关键词： computer vision

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Programmed Number Plate recognition using Deep Learning 4

Programmed Number Plate Recognition using Deep Learning

引用

4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Ananthi, M. Fathima, Afrin Ruqayyah M. Srigomathi, S. Abirami, R.K. Computer Science and Business Systems Sri Sairam Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9798350305258

Automatic License Plate recognition (ALPR) systems have become increasingly popular in recent years, as they provide a quick and efficient way to identify vehicles. the PNPR project aims to develop a deep learning-based ALPR system that can accurately detect and recognize license plates in real-time. the system utilizes state-of-the-art computer vision techniques to extract license plate information from CCTV camera feeds. the captured license plate information is then processed and compared to a database of registered plates to determine if the vehicle is authorized or not. the project's primary objective is to develop an accurate and reliable system that can aid law enforcement agencies in identifying stolen, unregistered, or unauthorized vehicles. the proposed PNPR system is designed to be scalable and can be deployed in various settings, including parking lots, toll booths, and border crossings. the results demonstrate that the PNPR system achieves high accuracy and can reliably detect and recognize license plates in real-time. the proposed system can potentially revolutionize how law enforcement agencies handle vehicle identification and monitoring. © 2023 IEEE.

关键词： Automatic number plate recognition computer vision Convolutional neural networks Deep learning image processing License plate recognition Object detection Programmed number plate recognition Security applications Surveillance systems Traffic management Vehicle tracking

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image saliency detection method based on multi-feature maps fusion 4

Image saliency detection method based on multi-feature maps ...

引用

4th international conference on computer Graphics, image, and Virtualization, ICCGIV 2024

作者： Li, Xiaoli Liu, Yunpeng Zhao, Huaici 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 Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9781510683242

In this research, we introduce an innovative saliency detection algorithm, comprising three essential steps. Firstly, leveraging fully convolutional networks with aggregation interaction modules, we generate an initial saliency map. Secondly, we extract hand-craft and deep features to express the image, then use manifold ranking method to construct saliency maps. Ultimately, by integrating the outcomes from preceding stages, we generate the final saliency map. Experimental findings demonstrate that our method surpasses twelve cutting-edge saliency detection techniques in terms of precision, recall, F-measure, and MAE value metrics." © 2024 SPIE.

关键词： Feature extraction

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Design and Research on Intelligent Construction Technology Based on computer vision 4

Design and Research on Intelligent Construction Technology B...

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4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Zhang, Yuhui Lv, Mengdi Sun, Qiming Tianjin University Tianjin300072 China Southeast University Architectural Design and Research Institute Co. Ltd Jiangsu Nanjing210096 China Swiss Federal Institute of Technology in Zurich Stefano-Franscini Platz 1 ZurichCH-8093 Switzerland

ISBN: (纸本)9798400708831

this paper aims to study the intelligent construction technology based on computer vision, which applies image processing and analysis technology in the construction process to improve construction efficiency and quality. First, key preprocessing steps for images including image blurring, image enhancement, and threshold segmentation are discussed to improve image quality and accuracy of feature extraction. then, specific applications of computer vision in concrete crack identification, steel component weld identification, and displacement measurement in construction are studied. For concrete crack identification, methods for crack skeleton extraction and length calculation as well as an overall detection framework for cracks are proposed. For steel component weld identification and displacement measurement, automation and precision in identification and measurement are achieved through computer vision technology. the research in this paper will provide theoretical support and practical reference for the application of intelligent construction technology, and contribute to improving the quality and efficiency of construction. © 2023 ACM.

关键词： computer vision

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MDFIDNet: Multi-domain Feature Integration Denoising Network 27th

MDFIDNet: Multi-domain Feature Integration Denoising Network

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27th international conference on pattern recognition, ICPR 2024

作者： Das, Debashis Maji, Suman Kumar Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

ISBN: (纸本)9783031781247

In the realm of computer vision, image denoising remains a formidable challenge with profound implications for fields like medical imaging, remote sensing, and photography. Despite notable advancements in deep learning, there are enduring challenges: current convolutional neural networks (CNNs) frequently struggle with training complexities due to their emphasis on increased network depth. At the same time, these networks often fail to adequately consider the crucial role of gradient information in the denoising process. Furthermore, there is a distinct gap in leveraging transform domain analysis in image denoising. this study addresses these limitations with MDFIDNet, a novel triple-phase attentive fusion network tailored for image denoising. MDFIDNet integrates three independent feature extraction pipelines: a frequency domain processing pipeline (FDP) enhanced by a multi-scale convolutional attention Block (MSCAB), a spatial domain processing pipeline (SDP) focusing on detail feature preservation, and a gradient-domain processing pipeline (GDP) driven by multidirectional gradient information. Experimental validation demonstrates that MDFIDNet surpasses existing benchmarks, exhibiting robust performance across diverse datasets. Comprehensive ablation studies underscore the individual contributions of each network component, elucidating the novel advancements that underpin MDFIDNet’s superior denoising efficacy. the source code and further details are available in the https://***/debashis15/MDFIDNet. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Optimization Study of Convolutional Neural Network Model Based on computer Simulation Context 4

Optimization Study of Convolutional Neural Network Model Bas...

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4th international conference on Signal processing and computer Science, SPCS 2023

作者： Zhang, Xuxu Hua, Jikai Department of Automation Tsinghua University China Department of Economics Moscow State Normal University Russia

ISBN: (纸本)9781510672505

Convolutional Neural Network (CNN) is the most successful mathematical model of an artificial neural network used in the fields of computer vision, image recognition, and classification. High-performance neural network structures are large in size, and a large number of multiplicative-additive calculations are required for a complete inference process. Training out a high-performance convolutional neural network model requires tens of times more computation than the inference process. the current theoretical and technical level cannot achieve a model that can be universally applied to all domains, and different application scenarios require designing specific neural network structures and collecting specific data sets. Large computing power requirements and high-quality data acquisition are two key components for training to obtain high-performance convolutional neural networks. © 2023 SPIE.

关键词： Convolutional neural networks

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