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检索条件"任意字段=7th International Conference on Image Analysis and Recognition, ICIAR 2010"

共 1068 条记录，以下是1-10 订阅

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Research on Automatic Driving Safety image recognition Based on Deep Learning 7

Research on Automatic Driving Safety Image Recognition Based...

引用

7th international conference on Automation Electronics and Electrical Engineering

作者： Zhang, Xiaoqing Tianjin Univ Technol Tianjin Peoples R China

ISBN: (纸本)9798350377040;9798350377033

the research is based on the data set BDDI00K, which can well reflect the complex traffic scenarios in the real situation, and build YOLOv4 based on the uncertainty regression of the prediction box to establish the reliability relationship mapping between the prediction box and the real box, so that the model can better detect complex traffic scenarios. In order to overcome the shortcomings of traditional detection methods and meet the actual driving environment, a traffic sign and pedestrian detection method based on YOLOv4 is proposed to provide data support for intelligent analysis and decision-making of safe operation of automatic driving, and provide reliable guarantee for eliminating hidden dangers of safe operation of automatic driving.

关键词： Deep learning Autonomous driving image recognition

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Decoding Emotions: Integrating EEG Signals and Facial Expressions for Advanced Multimodal Emotion recognition 7

Decoding Emotions: Integrating EEG Signals and Facial Expres...

引用

IEEE 7th international conference on Advanced Technologies, Signal and image Processing (ATSIP)

作者： Moussaoui, Khouloud Farah, Mohamed Univ Manouba Riadi Lab RIADI LR99ES26 ISAMM Univ Campus Manouba 2010 Tunisia

ISBN: (纸本)9798350351491;9798350351484

Emotion recognition is widely applied in medicine, education, and human-computer interaction, with three main approaches: non-physiological, physiological, and hybrid signals. Hybrid methods show promise while non-physiological signals are easily manipulable. Our study proposes a hybrid approach that combines EEG and facial expression data using decision-level fusion. Validating our approach using the DEAP database, we focused on binary classifications for alertness and valence. Our model classifies emotions into four categories: HVLA, HVHA, LVLA, and LVHA. From the dataset, we extracted 17 features from 32 EEG channels across 5 frequency bands for each subject. We applied SVM with the RBF kernel and achieved an accuracy of 54.49%. For facial expression classification, we preprocessed frames from the tests of each subject and used CNN to obtain a validation accuracy of 68.36%. In the fusion step, we combined the predicted probabilities of the four labels from the two unimodal classifiers using weighted averaging to calculate the average predicted probabilities for the final emotion classification. Our thorough approach and strong results make a meaningful contribution to the field of emotion computing and emotion recognition.

关键词： Emotion recognition Affective computing Electroencephalography (EEG) Facial expressions Decision-level fusion DEAP database

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Research on Document image Binarization: A Survey 7

Research on Document Image Binarization: A Survey

引用

7th IEEE international conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Ho, Jinlong Liu, Min Xiamen University of Technology School of Opto-Electronic and Comunicationn Engineering Xiamen China

ISBN: (纸本)9798350384437

In the digital age, the majority of paper documents are converted into electronic formats for preservation purposes. However, the conversion process can result in significant degradation of the document images, which can subsequently lead to difficulties in reading, text recognition, and other digitisation processes. Document image binarisation is a crucial pre-processing step for automatic document recognition and data analysis. the effectiveness of this binarisation directly impacts the accuracy of the digitisation operation. this paper presents a summary of the research results based on traditional thresholding methods, image feature methods and some learning methods, as well as the evaluation indexes of binarisation performance. It also discusses the characteristics and limitations of these methods. Finally, it presents a summary of relevant methods for document image binarisation and points out several promising research directions. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

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Performance analysis of Parkinson Detection Techniques 7

Performance Analysis of Parkinson Detection Techniques

引用

IEEE 7th international conference on Advanced Technologies, Signal and image Processing (ATSIP)

作者： Ammous, Donia Bouzayen, Hajer Ben Ayed, Yassine Higher Inst Comp Sci & Multimedia Sfax Sfax Tunisia Natl Sch Engineers Sfax Sfax Tunisia Lab Elect & Informat Technol LETI Sfax Tunisia Multimedia InfoRmat Syst & Adv Comp Lab MIRACLE Sfax Tunisia

ISBN: (纸本)9798350351491;9798350351484

Parkinson's Disease (PD) presents significant challenges in timely diagnosis and management, driving the need for advanced automatic recognition systems. this comparative study explores various methodologies for PD detection, leveraging machine learning and signal processing techniques across diverse modalities. Key phases, including data collection, preprocessing, feature extraction, selection, and classification, are scrutinized. through comprehensive analysis of methodologies, datasets, and performance metrics, the study reveals promising accuracies in PD classification. However, challenges such as dataset availability and class imbalance persist. Future research avenues include multi-modal fusion, real-time monitoring, and telemedicine integration, aiming to enhance diagnostic accuracy and accessibility. Embracing these perspectives can accelerate the translation of research findings into clinical practice, improving outcomes for individuals with Parkinson's disease.

关键词： Parkinson's disease machine learning early detection

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Research on the Application of Graphic and image Processing Technologies Within the Context of Visual Communication System 7

Research on the Application of Graphic and Image Processing ...

引用

7th international conference on Computer Information Science and Application Technology, CISAT 2024

作者： Zeng, Dan Hubei University of Technology School of Art and Design Wuhan China

ISBN: (纸本)9798350375107

With the development of computer technology, graphics and image processing technology has become a key component of visual communication system. Graphics and image processing technology as a common means of computer technology, through the application of this technology to the visual communication system, can bring a new experience for people's vision. the paper discusses the principles of image enhancement, image segmentation, image recognition and other core technologies and their specific applications in visual communication. image enhancement aims to improve the quality of images and make them more suitable for specific applications. image segmentation aims to divide an image into multiple parts or regions, so that these regions have similar properties internally, while there are significant differences between different regions. the purpose of image recognition is to enable computers to understand and recognize the content in images. through empirical analysis, this study demonstrates the significant benefits of graphics and image processing technology in improving work efficiency, enhancing customer satisfaction and reducing design costs. image processing technology not only optimizes the design process, but also enhances the effect of visual communication, which has a positive impact on the development of related industries. © 2024 IEEE.

关键词： Customer satisfaction

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Overview of Underwater Object Detection Based on image Processing Techniques 7

Overview of Underwater Object Detection Based on Image Proce...

引用

7th international conference on Pattern recognition and Artificial Intelligence, PRAI 2024

作者： Aamir, Muhammad Gao, Jinxiong Zhang, Yonghui Tang, Hao Bhatti, Uzair Aslam College of Computer Science Huanggang Normal University Huanggang438000 China School of Information and Communication Engineering Hainan University Haikou570100 China

ISBN: (纸本)9798350350890

underwater object detection and recognition are critical technologies in marine science and engineering, enabling advancements in environmental monitoring, resource exploration, and autonomous underwater vehicles. the rapid progression of computing power, underwater robotics, and artificial intelligence has driven substantial academic and industrial interest in this domain. this paper presents a comprehensive overview of the current methodologies in image-based underwater object recognition, where optical images or videos captured under various lighting conditions are processed to detect and identify underwater objects. the discussion includes an analysis of the prevailing algorithms, a review of the contemporary research landscape, and an exploration of emerging trends. Additionally, this paper offers insights into the anticipated technological advancements that will shape the future of underwater object recognition. © 2024 IEEE.

关键词： Underwater imaging

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7th IEEE international conference on Advanced Technologies, Signal and image Processing, ATSIP 2024

7th IEEE International Conference on Advanced Technologies, ...

引用

7th IEEE international conference on Advanced Technologies, Signal and image Processing, ATSIP 2024

ISBN: (纸本)9798350351484

the proceedings contain 118 papers. the topics discussed include: driver action recognition in low-light conditions: a multi-view fusion framework;fuzzy logic obstacle avoidance controller for a mobile robot using lidar;modeling continuous emotions in text data using IEMOCAP database;detection and classification of neurodegenerative diseases by automatic speech analysis;a comparative study of derivative-based and wavelet-based approaches for epilepsy EEG analysis;a novel nonlinear intelligent control approach for optimizing solar PV systems in medical applications under dynamic conditions;access control based on roles and groups: case study building a secure smart hospital using BPMS;credit card fraud detection using synthetic minority oversampling technique and deep learning technique;and in-memory computing architecture for efficient hardware security.

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Advanced graph deep learning for High-dimensional image analysis: challenges and opportunities 7

Advanced graph deep learning for High-dimensional image anal...

引用

IEEE 7th international conference on Advanced Technologies, Signal and image Processing (ATSIP)

作者： Hanachi, Refka Sellami, Akrem Farah, Imed Riadh Dalla Mura, Mauro Univ Manouba RIADI Lab Manouba 2010 Tunisia Univ Lille Cent Lille CNRS UMR 9189 CRIStAL F-59000 Lille France Grenoble INP GIPSA Lab CNRS Grenoble France

ISBN: (纸本)9798350351491;9798350351484

High-dimensional image analysis, such as Hyperspectral Imaging (HSI) data, poses unique challenges due to their high dimensionality and non-Euclidean structures, making their analysis and classification complex. In this study, we explore the use of both graph deep learning (GDL) and multi-view graph representation learning for HSI classification. Furthermore, we present our proposed approach of multi-view Graph Convolutional Networks (GCNs) and how it leverages multiple views of the data by combining spectral and spatial features to improve classification accuracy. We discuss then specific challenges encountered when training our model on large HSIs, including managing large-scale graph data. We also discuss promising opportunities to overcome these challenges. By highlighting the challenges and opportunities associated with GDL and multi-view GCN usage for HSI classification, this study aims to shed light on recent developments and future prospects in this rapidly evolving field.

关键词： Graph deep learning 3D images High-dimensional image analysis Graph partitioning Large-scale graph data

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Layer-by-layer Learning In image Processing For Unmanned Aerial Vehicle 7

Layer-by-layer Learning In Image Processing For Unmanned Aer...

引用

7th IEEE international conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Sineglazov, Victor Chynnyk, Petro NTUU Igor Sikorsky Kyiv Polytechnic Institute Dept. of Artificial Intelligence Kyiv Ukraine

ISBN: (纸本)9798331534141

this work explores an innovative approach to image processing that provides high efficiency and accuracy in computer vision tasks. In this work, step-by-step learning of quantum machine learning models is considered, in which each layer of the model specializes in specific functions, which significantly improves the quality of image recognition and classification. Special attention is paid to the adaptation of this approach to real-time conditions important for UAVs, where the processing of large volumes of data must take place under conditions of limited resources. the results of experiments are presented, which demonstrate the advantages of step-by-step learning in comparison with traditional methods of image processing, which allows to achieve higher accuracy and speed of data analysis during various tasks, such as monitoring, mapping and object detection in the field. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Geometric Deep Learning Techniques for Analyzing Brain 3D Meshes 7

Geometric Deep Learning Techniques for Analyzing Brain 3D Me...

引用

IEEE 7th international conference on Advanced Technologies, Signal and image Processing (ATSIP)

作者： Ayad, Mariem Sellami, Akrem Farah, Imed Riadh Dalla Mura, Mauro Univ Manouba RIADI Lab Manouba 2010 Tunisia Univ Lille Cent Lille CNRS UMR 9189CRIStAL F-59000 Lille France Grenoble INP CNRS GIPSA Lab Grenoble France

ISBN: (纸本)9798350351491;9798350351484

Geometric deep learning (GDL) has emerged as a powerful paradigm for analyzing complex data represented in non-Euclidean domains. In the field of neuroimaging, 3D meshes have become a prevalent representation for capturing the intricate structures of the brain. this survey paper provides a comprehensive overview of the recent advancements and techniques in using GDL to analyze brain 3D meshes. We systematically review the state-of-the-art methodologies employed in tasks such as segmentation, and classification of brain meshes. Additionally, we discuss the challenges and opportunities in this rapidly evolving field, including data scarcity, interpretability, and scalability. By synthesizing insights from diverse research efforts, this survey aims to guide researchers and practitioners toward a deeper understanding of the application of GDL in neuroimaging and pave the way for future breakthroughs in brain analysis.

关键词： Geometric deep learning brain analysis 3D meshes medical imaging

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