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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861-870 订阅

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A Contemporary Review of Interpolation-based Reversible Data Hiding

A Contemporary Review of Interpolation-based Reversible Data...

引用

IoT, Communication and Automation Technology (ICICAT), international conference on

作者： Supriya Pandey Shrawan Kumar Pandey Abhinandan Tripathi Department of Computer Science & Engineering Buddha Institute of Technology Gorakhpur India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Reversible data hiding (RDH) has become a critical topic in modern information security, as it allows for secure communication by embedding secret data into a cover medium. Importantly, RDH also ensures that the hidden data can be extracted while fully preserving the real content at the receiver's end, a process known as lossless recovery. RDH is particularly well-suited for applications in medical imaging, such as MRI and CT scans, as well as military uses. In recent years, several RDH techniques have been developed, including difference expansion, histogram shifting (HS), prediction error expansion (PEE), and interpolation. This review paper focuses on various RDH strategies that leverage interpolation techniques to achieve high visual quality (VQ) and data embedding capacity, with ongoing efforts by researchers to enhance both simultaneously.

关键词： Surveys Military communication Image quality Interpolation visualization Histograms Reviews Magnetic resonance imaging Information security Biomedical imaging

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CNN based Image-Denoising Techniques- A Review

CNN based Image-Denoising Techniques- A Review

引用

Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Amal Agarkar Sudhanshu Shrivastava Dhruva Bangde Dhiraj Bhise IT Department SVKM'S NMIMS MPSTME Shirpur India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Image denoising is a fundamental task within the realms of computer vision and image processing, with the objective of removing unwanted noise while preserving essential characteristics of the image. Convolutional neural networks (CNNs) have exhibited impressive capabilities across a range of computer vision tasks, with image denoising being one notable application. This research study gives a thorough overview of the many image denoising methods when CNN is used, with a particular emphasis on the developments and applications of this fascinating area. The importance of image denoising in numerous applications, including medical imaging, surveillance, and photography, is highlighted in the paper's introduction. By systematically examining CNN-based denoising techniques and their practical uses, the objective of this research study is to offer researchers and practitioners a thorough comprehension of the advancements in CNN-based image denoising application.

关键词： Training computer vision visualization Reviews Surveillance Noise reduction Noise

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Automatic Segmentation of Thyroid Nodule from Ultrasound Images using Spatial-Channel Attentive U-Net 2

Automatic Segmentation of Thyroid Nodule from Ultrasound Ima...

引用

2nd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2022

作者： Song, Shuang Liu, Linlin Yu, Ming-An Xiao, Ruoxiu School of Computer Science and Engineering Beihang University Beijing100091 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Department of Interventional Medicine China-Japan Friendship Hospital Beijing100029 China Shunde Graduate School University of Science and Technology Foshan Beijing100024 China

ISBN: (纸本)9781510660571

The ultrasound image is a commonly used imaging modality to treat thyroid nodules due to its rapid imaging speed and the ability for multiple anatomical and soft-tissue visualization. However, considering the variability of the position, shape, size and intensity of nodules, the accurate segmentation of thyroid nodules is challenging. In this paper, we proposed an automatic segmentation network of thyroid nodules by combining fast-RCNN and spatial-channel attentive U-Net. In our model, fast-RCNN is firstly utilized to identify the rough position of nodules. The predicted boxes are then utilized to crop the image patches containing only nodules and then build a new database. Next, a spatial-channel attentive U-Net is designed and trained to realize the nodule segmentation. By comparing the proposed network with other state-of-the-art models, we can gain superior results in discarding similar non-nodule structures and preserving the boundary information of nodules. © 2022 SPIE.

关键词： Image segmentation

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Pneumonia Detection on Chest X-ray Using Deep Convolutional Neural Networks

Pneumonia Detection on Chest X-ray Using Deep Convolutional ...

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Electrical, computer and Energy Technologies (ICECET), international conference on

作者： Abdulai Abdul-Malik Dason Rose-Mary Owusuaa Mensah Gyening Kate Takyi Linda Amoako Banning Eldad Antwi-Bekoe Michael Eshun Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana Information Technology Education Akenten Appiah-Menka University of Skills and Training and Entrepreneurial Development Kumasi Ghana Department of Computer Science Brock University St Catherines Canada

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

The manual interpretation of medical images in healthcare facilities often delays patient diagnosis. This process is characterized by slow, time-consuming, costly, and labor-intensive procedures. Pneumonia, a severe respiratory infection affecting the lungs, poses a significant threat, necessitating prompt detection and treatment to prevent fatalities. This study introduces an automated method utilizing deep learning to diagnose pneumonia. An approach employing a simplified CNN architecture has been devised, requiring less computational power. This approach can be particularly beneficial for healthcare facilities with limited resources. Experimentation conducted on a public image dataset yielded a model accuracy of 0.95. The study's findings demonstrate the proposed technique's efficacy, surpassing existing CNN models' accuracy and computational efficiency. This paper underscores the significance of deep learning in diagnosing and ensuring timely treatment for pneumonia.

关键词： Deep learning visualization Pneumonia Accuracy Computational modeling computer architecture Computational efficiency Convolutional neural networks Medical diagnostic imaging X-ray imaging

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Boosting Wavelet-Based Image Fusion Using Equal Weighted Average of DSIHE and CLAHE

Boosting Wavelet-Based Image Fusion Using Equal Weighted Ave...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Nikhil Soniminde Maheshwari Biradar D Y Patil International University Pune Pimpri Chinchwad College of Engineering Pune India School of Computer Science Engineering and Applications (SCSEA) D Y Patil International University Pune India

Image fusion remains fundamental in diverse technical domains such as computer vision, remote sensing, medical imaging, and military applications. With the rapid evolution of technology, the significance of image fusion in research is escalating steadily. Image fusion is paramount across multiple fields as it amalgamates data from numerous images or imaging modalities, creating a unified composite image. Wavelet image fusion is a technique specifically designed to combine information sourced from multiple images at various scales and resolutions. This methodology harnesses the power of wavelet transform, a mathematical tool, to achieve this fusion. Wavelet image fusion finds extensive applications in sectors like remote sensing, medical imaging, computer vision, and surveillance, where amalgamating data from diverse sources is pivotal for decision-making, analysis, and visualization. However, it's important to note that wavelet-based image fusion, while powerful, could be more flawless. It can introduce undesirable artifacts such as ringing and blurring effects in the resulting fused images. To address these limitations, a novel approach has been proposed. This method aims to mitigate blurring effects by employing Wavelet fusion of Dualistic Sub Image Histogram Equalization (DSIHE) and Clipped Limited Adaptive Histogram Equalization (CLAHE). Experimental results were obtained from analyzing datasets, including the Multi-focus Image Dataset Lytro Multifocus Images Dataset, using performance metrics like entropy, NIQE, and BRISQUE from which Naturalness Image Quality Evaluator (NIQE) demonstrates the superior effectiveness of the proposed method.

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NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM

NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM

引用

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

作者： Zihan Zhu Songyou Peng Viktor Larsson Zhaopeng Cui Martin R. Oswald Andreas Geiger Marc Pollefeys ETH Zürich MPI for Intelligent Systems Tübingen Lund University State Key Lab of CAD&CG Zhejiang University University of Amsterdam Tübingen AI Center University of Tübingen Microsoft

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM. However, existing works either rely on RGB-D sensors or require a separate monocular SLAM approach for camera tracking, and fail to produce high-fidelity 3D dense reconstructions. To address these shortcomings, we present NICER-SLAM, a dense RGB SLAM system that simultaneously optimizes for camera poses and a hierarchical neural implicit map representation, which also allows for high-quality novel view synthesis. To facilitate the optimization process for mapping, we integrate additional supervision signals including easy-to-obtain monocular geometric cues and optical flow, and also introduce a simple warping loss to further enforce geometric consistency. Moreover, to further boost performance in complex large-scale scenes, we also propose a local adaptive transformation from signed distance functions (SDFs) to density in the volume rendering equation. On multiple challenging indoor and outdoor datasets, NICER-SLAM demonstrates strong performance in dense mapping, novel view synthesis, and tracking, even competitive with recent RGB-D SLAM systems. Project page: https://***/.

关键词： Tracking loops Surface reconstruction Simultaneous localization and mapping Three-dimensional displays Pipelines Cameras Rendering (computer graphics)

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CombiNeRF: A Combination of Regularization Techniques for Few-Shot Neural Radiance Field View Synthesis

CombiNeRF: A Combination of Regularization Techniques for Fe...

引用

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

作者： Matteo Bonotto Luigi Sarrocco Daniele Evangelista Marco Imperoli Alberto Pretto Department of Information Engineering University of Padova Italy FlexSight Via Prima Strada 35 Padova Italy

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

Neural Radiance Fields (NeRFs) have shown impressive results for novel view synthesis when a sufficiently large amount of views are available. When dealing with few-shot settings, i.e. with a small set of input views, the training could overfit those views, leading to artifacts and geometric and chromatic inconsistencies in the resulting rendering. Regularization is a valid solution that helps NeRF generalization. On the other hand, each of the most recent NeRF regularization techniques aim to mitigate a specific rendering problem. Starting from this observation, in this paper we propose CombiNeRF, a framework that synergically combines several regularization techniques, some of them novel, in order to unify the benefits of each. In particular, we regularize single and neighboring rays distributions and we add a smoothness term to regularize near geometries. After these geometric approaches, we propose to exploit Lipschitz regularization to both NeRF density and color networks and to use encoding masks for input features regularization. We show that CombiNeRF outperforms the state-of-the-art methods with few-shot settings in several publicly available datasets. We also present an ablation study on the LLFF and NeRF-Synthetic datasets that support the choices made. We release with this paper the open-source implementation of our framework.

关键词： Geometry Training Three-dimensional displays Annealing Perturbation methods Color Rendering (computer graphics)

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TacWgan-gp: Fabric Texture-Based Tactile Friction Coefficient Generation

TacWgan-gp: Fabric Texture-Based Tactile Friction Coefficien...

引用

international conference on computer Supported Cooperative Work in Design

作者： Baoshou Li Yu He Ge Yu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Generating tactile data from visual modality is an important technology in various fields, including haptic rendering, virtual reality, and robotics. This technique effectively bypasses the labor-intensive process of tactile data recording and addresses the limitations inherent in physical contact. However, existing generation methods exhibit limited consistency in generating multiple types of tactile data. To address this issue, we propose the TacWgan-gp cross-modal generation framework, which uses the WGAN-GP as the base, incorporates a center image condition constraint and L1 loss to regulate the generation quality. This framework is capable of generating spectrograms of friction coefficient signals from fabric texture images and ultimately converting them into one-dimensional friction coefficient signals using the Griffin-Lim algorithm. We utilize RMSE and MAE to quantify the differences between the generated spectrograms and reconstructed signals and their corresponding ground truth, and compare them with existing methods. The results show that our proposed method significantly outperforms existing methods in terms of generation accuracy and stability.

关键词： visualization Friction Metals Virtual reality Rendering (computer graphics) Fabrics Recording

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Unicrowd Simulator: Visual and Behavioral Fidelity For The Generation of Crowd Datasets

Unicrowd Simulator: Visual and Behavioral Fidelity For The G...

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IEEE international conference on Image Processing

作者： Niccolò Bisagno Antonio Luigi Stefani Nicola Garau Francesco De Natale Nicola Conci Department of Information Engineering and Computer Science Via Sommarive 9 Povo University of Trento Trento TN Italy

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

We introduce UniCrowd 1 , a human crowd simulator for the modeling of human-related dynamics. The simulator is accompanied by a meticulously collected dataset within its synthetic environment, along with a comprehensive validation pipeline. Leveraging simulation as a powerful tool for generating annotated data, UniCrowd addresses the increasing demand for large training datasets, mimicking both the behavioral and visual aspect of crowds. Recent advancements in rendering and virtualization engines have enhanced the simulators capabilities to represent complex scenes, encompassing environmental factors such as weather conditions, surface reflectance, and human-related events like actions and behaviors. The adaptability and the non-deterministic nature of the human behavioral module of UniCrowd, coupled with its 3D rendering represents an improvement over available crowd simulators. We demonstrate the suitability of our simulator and its associated dataset for various computer vision tasks. We highlight applications such as detection and segmentation, as well as specialized tasks including crowd counting, human pose estimation, trajectory analysis and prediction. 1 The simulator and the dataset can be accessed at ***/mmlabcvUniCrowd

关键词： Training visualization computer vision Rendering (computer graphics) Behavioral sciences Trajectory Task analysis

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Lab Color Model and Contrast Modification as a Tool for Improving the Quality of Medical Images

Lab Color Model and Contrast Modification as a Tool for Impr...

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Computational Intelligence Approaches and Applications (ICCIAA), international conference on

作者： Amer Abu-Jassar Mohammad Hamdan Kawther Hamash Sardor Boboyorov Vyacheslav Lyashenko Department of Computer Science College of Information Technology Amman Arab University Amman Jordan College of software engineering Amman Arab University Amman Jordan WellStar school of nursing Kennesaw state university Georgia USA Tashkent Medical Academy Termiz branch Termiz Uzbekistan Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper addresses the challenge of enhancing the quality of medical data interpretation, specifically using color microscopic medical images for effective visualization and further analysis. The method employed involves increasing contrast by adjusting the brightness intensity values within the Lab color model, which also considers the reference white point for horizontal light. To determine the parameters for the contrasting procedure, two strategies for selecting gamma correction of the input image are explored, based on the dynamics of the resulting image quality for various no-reference quality metrics. By analyzing these dynamics, the choice of correction conditions for the contrasting procedure can be justified, rather than relying on static metrics. The paper investigates the application of these proposed strategies on different types of images, presents a formal generalization of the strategies for selecting correction parameters, and offers practical recommendations for their use.

关键词： Measurement Image quality visualization Image color analysis Microscopy Computational modeling Data visualization Quality assessment Computational intelligence Biomedical imaging

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