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检索条件"任意字段=International Conference on Computer Graphics, Imaging and Visualization"

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Research on fall detection algorithm based on PPYOLOE

Research on fall detection algorithm based on PPYOLOE

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international conference on Virtual Reality and visualization (ICVRV)

作者： Yan Wan Xiao Hu YanZhen Wu Dept. College of Computer Science and Technology Donghua University Shanghai China

ISBN: (数字)9798331515447

ISBN: (纸本)9798331515454

Falls pose a serious challenge to the health and safety of the elderly population. In crowded public places, such incidents may also trigger secondary disasters such as stampede, so rapid identification of fall behavior is crucial for maintaining public safety. Traditional fall detection algorithms have significant deficiencies in terms of insufficient feature extraction, single detection method, and poor real-time performance, which make it difficult to cope with complex fall scenarios and real-time detection requirements. In order to overcome the shortcomings of traditional fall detection algorithms, this study improves the PPYOLOE algorithm. We optimize feature extraction using Depthwise Separable Convolution (DSConv) and introduce High-Level Screening-feature Fusion Pyramid Networks (HS-FPN) and Efficient Multi-Scale Attention (EMA) to enhance important features. The improved algorithm significantly improves the detection performance and computational efficiency. Compared with the traditional PPYOLOE algorithm, the fall detection algorithm designed in this paper shows significant advantages in detection accuracy and real-time performance. In the experiment, Our improved algorithm has achieved a 3.5% increase in accuracy on the mAP@0.5 metric, it reaches 94.4% and achieves 24fps on GPU, which realizes the high precision detection of fall events and meets the deployment requirements in low computation scenarios. The real-time and high precision of the algorithm makes it suitable for various monitoring scenarios, which improves the social security level and emergency response capability.

关键词： visualization Accuracy Surveillance Disasters graphics processing units Feature extraction Real-time systems Public security Fall detection Older adults

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Holographic Printing Method Based on Neural Graphic Primitives

Holographic Printing Method Based on Neural Graphic Primitiv...

引用

Artificial Intelligence and Automation Control (AIAC), international conference on

作者： Xinyi Shi Hebin Chang Department of Physics Capital Normal University Beijing China Department of Information Communication Army Academy of Armored Forces Beijing China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

This paper presents an innovative method for printing synthetic holographic stereogram, combining Instant Neural graphics Primitive (Instant-NGP) technology with Effective Perspective Images' Segmentation and Mosaicking (EPISM) technique. The method utilizes Instant-NGP's efficient neural radiance field model to rapidly construct implicit representations of 3D scenes through sparse sampling and render required images. These images are then used to produce holograms using EPISM holographic printing technology. This approach overcomes the difficulties associated with traditional light field acquisition, significantly improving efficiency and reducing limitations. Optical experiments confirm that the system can quickly generate the required exposure images and print high-quality holographic stereogram. Through computer programming, the procedural steps are modularized and automated, making the acquisition of high-quality synthetic holographic stereogram simple, fast, and end-to-end.

关键词： graphics Solid modeling Image segmentation Programming Holography Three-dimensional printing Optical imaging Neural radiance field Holographic optical components Light fields

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Object-based SLAM Using Superquadrics

Object-based SLAM Using Superquadrics

引用

IEEE/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Yifan Xing Noe Samano Wen Fan Andrew Calway School of Computer Science Visual Information Laboratory University of Bristol United Kindom Department of Bioengineering Imperial College London United Kindom

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Visual SLAM uses visual information, typically point features, to localise a camera and, at the same time, map the environment. In recent years, there has been interest in using scene-understanding capabilities to enhance the mapping process and object-level SLAM systems have appeared in response. However, most of the previous work is limited to prestored object models or pre-trained networks to represent the objects, which limits working scenarios or uses representations with limited scope, such as cubes or quadrics. To address this, we propose to use superquadrics as the object representation and, in this paper, present a proof of principle SLAM system in which object-based mapping is fully integrated with camera tracking via keyframe optimisation. The system was tested on simulated and real datasets, and the results show that the system can achieve lightweight and comparatively good object representation whilst also giving good camera trajectories estimates under certain scenarios.

关键词： Bundle adjustment visualization Simultaneous localization and mapping Shape Cameras Rendering (computer graphics) Probabilistic logic Trajectory Optimization Intelligent robots

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Streamlined U-Net for MRI Brain Tumor Segmentation with Streamlit visualization Interface

Streamlined U-Net for MRI Brain Tumor Segmentation with Stre...

引用

computer, Electronics & Electrical Engineering & their Applications (IC2E3), international conference on

作者： Vrishab Prasanth Davey Rohit Kumar Mandeep Guleria Yeswanth J B Akilandeswari. P School of Computing SRM Institute of Science and Technology KTR Dept. of Electrical Engineering National Institute of Technology Uttarakhand

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

Brain tumor segmentation from MRI scans is a critical process in medical diagnostics, influencing both the prognosis and the strategy of treatment for patients. Traditional methods of segmentation by radiologists are not only time-intensive but also prone to human error, necessitating the development of automated and precise segmentation techniques. This research introduces an optimized U-Net convolutional neural network architecture specifically adapted for the segmentation of brain tumors from MRI data. The proposed model is designed to enhance segmentation accuracy and is trained extensively on a diverse dataset with meticulous augmentation to ensure robust generalizability. Alongside the algorithmic contributions, this study also presents an interactive visualization tool developed using streamlit, which enables clinicians to dynamically interact with both the raw and processed MRI data. The interface boasts real-time segmentation capabilities, thereby providing an accessible platform for medical professionals to validate and utilize the segmentation output. The model is better at accurately drawing the edges of tumors, as shown by the results of the experiments. This shows that it could be used in clinical settings and is an improvement over current segmentation methods. The research encapsulates not only a methodological innovation in medical imaging analysis, but also a step forward in the practical utility of such technologies in a clinical environment.

关键词： Image segmentation Technological innovation Accuracy Magnetic resonance imaging Brain tumors Streaming media Brain modeling Real-time systems Medical diagnosis Reliability

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Heightmap Examiner. A Descriptive Tool for Education in Digital Cartography 23rd

<i>Heightmap Examiner</i>. A Descriptive Tool for Education ...

引用

23rd international conference on Human-computer Interaction (HCII)

作者： Hernandez-Ibanez, Luis A. Barneche-Naya, Viviana Univ A Coruna Adv Visualizat & Cartog Grp VAC videaLAB La Coruna 15071 Spain

ISBN: (纸本)9783030903282;9783030903275

This paper describes a graphic application named Heighmap Examiner designed to help educate students on the computer graphics techniques that constitute the core of visualisation modules in GIS software. It applies various pixel-based algorithms over elevation datasets or heightmaps to obtain topographic and cartographic representations such as those made in professional software. The code is open and organised following this educational standpoint to let students understand and modify it if needed, even including new features to the application. This way, they can learn how modern topographical applications deal with terrain data under a computer graphics perspective to obtain contour maps, hypsometric maps, sunlight simulation, rain watersheds, etc., and also any combination of those. This paper describes the software's overall organisation and the different algorithms applied to enhance performance and visual quality. The application has been used as part of the MSc curriculum in Civil Engineering being taught at our university.

关键词： Heighmap Cartography GIS education Topographical visualization

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An Optimized Deep Learning Model For Photographic Images And Photorealistic computer-Generated graphics Image Classification

An Optimized Deep Learning Model For Photographic Images And...

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Innovations in High Speed Communication and Signal Processing (IHCSP), international conference on

作者： Gangu Rama Naidu Chanamallu Srinivasa Rao Department of ECE University College of Engineering JNTUK University Kakinada AP India Department of ECE JNTUK University Kakinada AP India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

The differentiation between computer-generated images and photographs has emerged as a significant study issue in the image forensics field due to the heightened visual realism of artificial images. Existing research on distinguishing these images typically needs a superior quantity of labeled data for training, high computational resources, and also suffers from inaccurate distinction as well as image quality issues. Therefore, an Adaptive Gazelle Optimization Algorithm Enabled Deep convolutional neural network model (AdGO-DCNN) is developed in this research to resolve the existing difficulties. The DCNN classifier distinguishes the images effectively while reducing the risk of overfitting. The Adaptive Gazelle Optimization (AdGO) helps in the model’s performance improvement with minimized computational time. The experimental outcomes of the research show an excellent performance for the proposed model indicating the model’s efficiency in distinguishing computer-generated and photograph images by attaining an accuracy of 96.54%, 0.09 False negative rate (FNR), 0.07 False positive rate (FPR), 97.04% F-score, 95.18% Precision, and 98.98% recall and 0.964 ROC exceeded other existing approaches.

关键词： Deep learning Adaptation models visualization Image forensics Computational modeling Signal processing algorithms Classification algorithms Optimization Overfitting Image classification

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Revolutionizing Sentiment Analysis: Accelerated Data Science Approaches for Reddit Submissions

Revolutionizing Sentiment Analysis: Accelerated Data Science...

引用

India Council international Subsections conference (INDISCON), IEEE

作者： Shiya Mer Shatakshi Saxena Arun Singh Pundir Computer Science Dept Thapar University Patiala Punjab

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

Social media platforms has been demonstrated as an effective method for gathering user feedback to support requirements engineering and software evolution. Social networking services such as Reddit and Facebook have achieved widespread global popularity as platforms where individuals can establish public profiles, engage with genuine friends, share their interests and viewpoints, and publish messages on many subjects. Every post is labeled with tags for the purpose of sifting. In the Reddit community, these specific tags are referred to as flairs. This article explores the application of sentiment analysis to Reddit comments, with a specific focus on comparing the speed of computational operations utilizing CPU and GPU-based methods. Data extraction from the Reddit platform was accomplished by utilizing web scraping techniques, with the assistance of the Python-based Reddit API Wrapper (PRAW). More precisely, the investigation focused on gathering submissions from the r/funny subreddit. A thorough assessment of the performance of well-established sentiment classification models, such as Random Forest [9], Logistic Regression [11], KNN [14], and SVM [14], was carried out. This study goes beyond traditional methods that rely on central processing units (CPUs) and instead investigates the possible advantages of using graphics processing units (GPUs) for computation. The study specifically emphasizes the benefits of using the Rapids framework for GPU computing. In addition, the research provides a comprehensive data visualization method using Tableau dashboards [5] to improve the clarity and understanding of the results. This work utilizes the extensive collection of user-generated content on Reddit to enhance sentiment analysis techniques in social media settings, providing significant insights for requirements engineering and software evolution processes.

关键词： Support vector machines Sentiment analysis Logistic regression Social networking (online) Computational modeling graphics processing units Data visualization Software Central Processing Unit Requirements engineering

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Comparison of Learning Achievement with Interactive Digital Lessons on Using a Program for Basic Photo Editing: Case Study of Daruna Vocational Certificate Students

Comparison of Learning Achievement with Interactive Digital ...

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Cybernetics and Innovations (ICCI), international conference on

作者： Burin Narin Jirayu Nimnual Phatthranit Srisakonsub Department of Computer Education Muban Chombueng Rajabhat University Ratchaburi Thailand

ISBN: (数字)9798350370096

ISBN: (纸本)9798350370102

This paper presents experimental research aimed at comparing the academic achievement of students who have studied with an interactive digital lesson on using basic image editing programs designed for vocational certificate students. Additionally, the study seeks to assess students' satisfaction with these lessons. The research population and sample consist of students enrolled in the computer graphics course during Semester 1/2023 at Daruna Ratchaburi Vocational College, selected through purposive sampling. The research utilized the following tools: 1) an interactive digital lesson on using a program for basic image editing for Vocational Certificate students 2) an assessment form for evaluating the quality of interactive digital lessons, 3) an academic achievement test and 4) an assessment of student satisfaction with the interactive digital lessons. The results of the research indicate that the academic achievement of students who participated in the interactive digital lessons on the use of basic image editing programs for vocational certificate students improved significantly compared to their performance before studying, with statistical significance at the 0.05 level. Moreover, the satisfaction level of students with the digital lessons was found to be high (x̅ = 4.54, S.D. = 0.34)

关键词： visualization Technological innovation Systematics Education Sociology Psychology Media

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A Survey on Breast Cancer Prediction using Various Deep Learning and Machine Learning Techniques

A Survey on Breast Cancer Prediction using Various Deep Lear...

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international conference on Communication and Electronics Systems (ICCES)

作者： G. Gurupakkiam M. Ilayaraja Department of Computer Applications Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Anand Nagar Krishnankoil

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Breast Cancer continues to be a predominant contributor to mortality associated with cancer among women on a global scale. To improve survival rates and treatment consequences, it's necessary to detect issues early and create an accurate diagnosis. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques have shown great promise in medical diagnostics, offering the potential to enhance the accuracy and efficiency of breast cancer detection and prognosis. This research work provides breast cancer prediction using deep learning and machine learning studies on mammograms, ultrasounds, magnetic resonance imaging, and digital pathology images conducted over the past decade. An evaluation of studies from the past ten years highlights how important deep learning and machine learning have been in better patient outcomes and enlarges the accuracy of breast cancer detection. The automation of medical image assessment, machine learning, and deep learning technologies facilitate radiologists and pathologists in rendering more knowledgeable and accurate decisions. This reduction in the probability of human errors culminates in superior diagnostic accuracy and treatment methodologies, thereby ultimately improving the quality of patient care.

关键词： Deep learning Surveys Accuracy Ultrasonic imaging Computational modeling Predictive models Rendering (computer graphics) Breast cancer Data models Random forests

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Image Indexing Approaches for Enhanced Content-Based Image Retrieval: An Overview

Image Indexing Approaches for Enhanced Content-Based Image R...

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Ubiquitous Networking (UNet), international conference on

作者： Abdelkrim Saouabe Said Tkatek Hicham Oualla Imad Mourtaji Computer Science Research Laboratory Faculty of Sciences IbnTofail University Kénitra Morocco AKKODIS Research Paris France

ISBN: (数字)9798350356144

ISBN: (纸本)9798350356151

The effective management and analysis of vast collections of digital images rely heavily on image indexing. This process facilitates image access through the creation of a textual description or digital signature using metadata and visual data such as color, shape, and texture. Manual and automatic indexing are two commonly used techniques for generating image indexing. This article explores the three main approaches used by current image retrieval systems, namely text-based, content-based, and ontology-based approaches. Specifically, this paper focuses on Content-Based Image Retrieval (CBIR) systems, which rely on the visual content of images to search for corresponding images in a target image database based on their features. A comprehensive literature review of CBIR approaches is presented to demonstrate the advantages of this method for managing digital images. Additionally, this paper reviews the various applications of automatic image indexing, such as photographic archive management, Digitization of audiovisual archives, e-commerce, social media, and industry. Automatic image indexing can help in organizing products by category, improving product searches, and detecting inappropriate or offensive images.

关键词： Measurement visualization Social networking (online) Shape Reviews Digital images Image retrieval Metadata Rendering (computer graphics) Indexing

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