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

共 12125 条记录，以下是871-880 订阅

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TextMesh: Generation of Realistic 3D Meshes From Text Prompts

TextMesh: Generation of Realistic 3D Meshes From Text Prompt...

引用

international conference on 3D imaging, Modeling, Processing, visualization and Transmission (3DIMPVT)

作者： Christina Tsalicoglou Fabian Manhardt Alessio Tonioni Michael Niemeyer Federico Tombari ETH Zurich Google Technical University of Munich

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

The ability to generate highly realistic 2D images from mere text prompts has recently made huge progress in terms of speed and quality, thanks to the advent of image diffusion models. Naturally, the question arises if this can be also achieved in the generation of 3D content from such text prompts. To this end, a new line of methods recently emerged trying to harness diffusion models, trained on 2D images, for supervision of 3D model generation using view dependent prompts. While achieving impressive results, these methods, however, have two major drawbacks. First, rather than commonly used 3D meshes, they instead generate neural radiance fields (NeRFs), making them impractical for most real applications. Second, these approaches tend to produce over-saturated models, giving the output a cartoonish looking effect. Therefore, in this work we propose a novel method for generation of highly realistic-looking 3D meshes. To this end, we extend NeRF to employ an SDF backbone, leading to improved 3D mesh extraction. In addition, we propose a novel way to finetune the mesh texture, removing the effect of high saturation and improving the details of the output 3D mesh.

关键词： Surveys Geometry Solid modeling Three-dimensional displays Rendering (computer graphics) Optimization Mesh generation

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BioTwinNet: Dual-Stream Multilevel Feature Fusion for Classification of SARS-CoV-2 and Influenza Virus Variants via Genomic Image Processing

BioTwinNet: Dual-Stream Multilevel Feature Fusion for Classi...

引用

Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Vatsal Shah Love Fadia Mohammad Hassanzadeh Majid Ahmadi Jonathan Wu Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The fast evolution and high transmissibility of emerging infectious illnesses like Influenza and SARS-CoV-2 pose serious public health problems. In order to effectively classify SARS-CoV-2 and Influenza variations, this study presents BioTwinNet, a unique dual-stream deep learning framework that integrates multilevel feature fusion with genomic image processing. Using methods like Wavelet Analysis, Discrete Cosine Transform, and Power Spectral Density, DNA sequences were converted into visual representations. BioTwinNet, which makes use of the depth capabilities of ResNet architectures and the hierarchical feature extraction of Convolutional Neural Networks, was used to evaluate these genomic images. By using Discrete Cosine Transform-Power Spectral Density characteristics, this method achieves an impressive classification accuracy of 97.42%, overcoming the difficulties caused by differences in sequence length.

关键词： Deep learning visualization Genomics Influenza Signal processing Wavelet analysis Coronaviruses Convolutional neural networks Bioinformatics Biomedical imaging

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Enhancing Classification Power: Tree Strength-Infused Enriched Random Forest

Enhancing Classification Power: Tree Strength-Infused Enrich...

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international conference on Computing and Networking Technology (ICCNT)

作者： Vikas Jain Tej Bahadur Chandra Atul Kumar Srivastava SCSET Bennett University Gr. Noida India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Over the last two decades, Random forest (RF) has been one of the well-known and most exploited ensemble-based machine learning approaches. It has been used for computer vision, pattern recognition, medical imaging, and many other fields. However, it has always been a challenging task to come up with an approach that will lead to constructing a more reliable RF. In this study, we used a concept of tree strength to construct the RF, called as Enriched Random Forest (ERF). As a feature extraction, Bag of Visual Words (BoVW) is used, and Grey Wolf Optimization (GWO) as a feature selection during the construction of the ERF. The suggested method has been evaluated using the MNSIT, Caltech-101, Caltech-256, and UCI repositories—all of which are well-known and openly accessible. The outcomes of the experiments indicate that the ERF has achieved better results than other RF-based approaches.

关键词： Radio frequency visualization computer vision Forestry Feature extraction Pattern recognition Reliability Random forests Optimization Biomedical imaging

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FractureAI Revolutionizing Bone Fracture Detection with Deep Learning CNNs for Precision Medical imaging

FractureAI Revolutionizing Bone Fracture Detection with Deep...

引用

Automation and Computation (AUTOCOM), international conference on

作者： I. Sudha P.S. Ramesh P Durgadevi G. Sundari Senthilvadivu. S Sudha Narang Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani campus Chennai-26 Department of CSE C. Abdul Hakeem College of Engineering and Technology Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai TamilNadu India Maharaja Agrasen Institute of Technology Rohini Delhi

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Detection of bone fractures is part and parcel of clinical diagnosis, previously relied on radiological interpretation of X-ray or CT scans. However, manually reviewed system involves human intervention and it is vulnerable to reveal more false negatives or delayed at revealing certain fractures. To counter these challenges, this research proposes Fracture AI, a deep learning system that employs CNNs, specifically ResNet-50 for fracture identification and categorization. With the help of transfer learning and techniques such as Grad-CAM for fracture localization, the system can reach remarkable results with the accuracy of 96.2%, precision of a 97.9%, recall of a 96.9%, and the F1-score of a 97.4%. The results presented here show how Fracture Ai can enhance the diagnostic process enabling the identification of fractures in a shorter time, especially in emergency departments, has been clearly shown here and the AI solution is accurate.

关键词： Deep learning Training visualization Accuracy Computed tomography Transfer learning Bones X-ray imaging Medical diagnostic imaging Testing

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WaveFilter: Advanced imaging for Marine Microplastic Monitoring

WaveFilter: Advanced Imaging for Marine Microplastic Monitor...

引用

Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Deepali Jadhav Amruta Amune Bhairavi Shirsath Suraj Chavan Prathmesh Sonawane Tanishk Shrivastava Ishan Shivankar Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

YOLOv5 has proven to be an efficient deep learning model for the detection of microplastics in aquatic environments. It is compact, with a total number of 214 layers and 7,022,326 parameters, while its model size is approximately 14.4 MB. Extensive performance analysis was done that states that the model has a precision of 79.8% and a recall of 67.1%, hence proving efficiency in the detection of instances of microplastics. The performance metrics are that the model offers a mAP of 72.1% at IoU=0.5 and averaged mAP of 34.1% averaged across different IoU values. These also validate the strength of YOLOv5 to detect microplastic with complex backgrounds which could be applied to real-time, automated, computer vision-based frameworks for the detection of marine plastic pollution. These promising results from the study have opened further vistas for development in environment monitoring and conservation.

关键词： YOLO Deep learning Analytical models visualization Accuracy Computational modeling Real-time systems Robustness Monitoring Plastic waste

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3D Reconstruction of Colonic Polyps: A Methodology for Improved visualization and Analysis

3D Reconstruction of Colonic Polyps: A Methodology for Impro...

引用

international conference on Electrical, computer and Communication Engineering (ECCE)

作者： Ishtiaque Ahmed Pranto Saha Md. Saimim Islam Khan Hamim Irfan Ahmad Alvi Md. Zahid Hasan Md. Hasan Imam Bijoy Department of Computer Science and Engineering Health Informatics Research Laboratory (HIRL) Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Polyps are critical abnormalities that may indicate the initial phases of colon cancer, making accurate detection and analysis essential in medical diagnostics. Traditional 2D medical imaging techniques provide limited insight into the shape and structure of polyps, which is where 3D reconstruction can play a transformative role. By converting 2D polyp images into 3D models, healthcare professionals can gain enhanced visualization, enabling better diagnosis, treatment planning, and surgical precision. This study demonstrates a robust process for reconstructing polyp images in 3D using the Kvasir-Seg dataset, which includes 1,000 polyp images with expertly annotated ground truth masks. Our methodology includes the generation of depth maps using the Intel DPT-Large model, the extraction of point clouds with a customized approach, and the construction of precise mesh objects with Open3D. We extract geometric properties, including linearity, planarity, and curvature change, from the point clouds and meshes for a more in-depth analysis of the 3D objects. The unsupervised analysis of the resulting density plots indicates the presence of two distinct clusters within the feature distributions, highlighting the significance of 3D reconstruction for improved polyp characterization. Furthermore, the evaluation included 2D features such as edge density, contrast, dissimilarity, homogeneity, energy, correlation, entropy, number of lines, circularity, and Gabor energy. The analysis of p-values reveals that the 3D features related to these two clusters show notably lower p-values, thereby demonstrating their significance in the comprehensive analysis.

关键词： Point cloud compression visualization Solid modeling Three-dimensional displays Accuracy Shape Surgery Transforms Planning Image reconstruction

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Time-series visualization of Twitter Trends 15

Time-series Visualization of Twitter Trends

引用

15th international Joint conference on computer Vision, imaging and computer graphics Theory and Applications (VISIGRAPP) / 11th international conference on Information visualization Theory and Applications (IVAPP)

作者： Konishi, Atsuro Hosobe, Hiroshi Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan Hosei Univ Fac Comp & Informat Sci Tokyo Japan

ISBN: (纸本)9789897584022

Twitter provides a function called "trend" that presents popular words and hashtags. Typically, one trend word or hashtag is related to thousands of tweets. It is difficult to understand such thousands of tweets in a short time by using the standard sort methods and the standard display method provided by Twitter. Most of previous studies analyzed and visualized tweets by using text-based clustering methods. However, these methods suffer from the accuracy of clustering results, because a typical tweet has only poor textual information. This paper presents a Twitter trend analysis system that combines retweet clustering and timeseries visualization to allow users to understand flows of topics in a Twitter trend in a short time. This system also provides a list of effective legends and a display of individual tweets with photos in order for users to further understand topics in a trend. To illustrate the effectiveness of this system, this paper presents the results of experiments on the analysis of Twitter trends related to a popular sport event and a popular music program.

关键词： Twitter Trends Retweet Time-series Visual Data Analysis and Knowledge Discovery

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A Timeline Metaphor for Analyzing the Relationships between Musical Instruments and Musical Pieces 15

A Timeline Metaphor for Analyzing the Relationships between ...

引用

作者： Kusnick, J. Khulusi, R. Focht, J. Janicke, S. Univ Leipzig Inst Comp Sci Image & Signal Proc Grp Leipzig Germany Univ Leipzig Inst Musicol Mus Instrument Museum Leipzig Germany Univ Southern Denmark Dept Math & Comp Sci Odense Denmark

ISBN: (纸本)9789897584022

Digitization projects make cultural heritage data sustainably available. However, while digital libraries may capture various aspects, relations across different sources often remain unobserved. In our project, musicologists aimed to relate musical instruments with historical performances of musical pieces, both contained in different sources. We defined a similarity measure taking instrumentation, temporal as well as geospatial metadata into account, with which we were able to hypothesize potential relations. We propose a novel time-line design that offers a specific semantic zoom metaphor enabling the collaborating musicologists to observe and evaluate the results of our similarity analysis. The value of our system for research in musicology is documented in three case studies.

关键词： Music visualization Timeline visualization Geovisualization Visual Design

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Universal Heterogeneous Accelerated Design of Intelligent Vision System Based on FPGA

Universal Heterogeneous Accelerated Design of Intelligent Vi...

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Advances in Electrical Engineering and computer Applications( AEECA), 2020 IEEE international conference on

作者： Zonghao Tian Xinan Lian Army Academy of Artillery and Air Defense Hefei China

ISBN: (纸本)9798350355260

With the increasing demand for edge devices to deploy intelligent vision systems, heterogeneous processors have replaced homogeneous processors due to their advantages of low power consumption, high parallelism, and low latency. We analyze the characteristics of image acquisition and object detection in intelligent vision systems and propose a universal heterogeneous acceleration method based on the resource-rich and flexible Field-Programmable Gate Array(FPGA). To enable rapid deployment of intelligent vision systems on the FPGA platform, various methods are applied, including IP design for image preprocessing, hardware acceleration operator encapsulation, and task scheduling. Experimental analysis indicates that the heterogeneous intelligent vision acceleration general design framework constructed in this study ensures clear imaging and real-time detection of targets within intelligent vision systems. This framework exhibits flexibility in application across diverse environments through parameter configuration, significantly reducing the cycle for translating various algorithms into engineering applications.

关键词： Encapsulation visualization Program processors Machine vision imaging Object detection Visual systems Real-time systems IP networks Field programmable gate arrays

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Aided Application of Neural Network-Fuzzy in Human Vision Refraction imaging

Aided Application of Neural Network-Fuzzy in Human Vision Re...

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Image Processing, computer Vision and Machine Learning (ICICML), international conference on

作者： Bowen Wu Department of Optometry College of Medical Technology and Nursing Yuanpei University of Medical Technology Hsinchu China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

With the rapid advancement of auto-refractor technology, refraction services have become readily available in most optometry stores. While auto-refractors offer speed and convenience, the measurements they provide are subject to a notable margin of error influenced by psychological and physical factors. Consequently, repetitive testing is required to obtain more accurate mean error values. Therefore, it is crucial to explore research on measurement methods that can complement auto-refractor measurements and validate refraction results. This will help ensure more reliable and precise outcomes in the field of refraction. The Liou eye model is employed to simulate the human eye and facilitate the development of optometric techniques. In this study, CODE V optical design software was utilized to calculate the visual imaging generated by personalized, individual human eye models. Subsequently, an innovative algorithm combining fuzzy logic and backpropagation neural networks was proposed for category recognition. The experimental results using this new approach achieved a recognition accuracy rate exceeding 98%, thereby enhancing the precision of optometry and enabling dual confirmation through computer-aided optometry.

关键词： Fuzzy logic Backpropagation visualization Accuracy Image recognition Codes Neural networks Software algorithms Software reliability Testing

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