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

共 12023 条记录，以下是211-220 订阅

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See In Detail: Enhancing Sparse-view 3D Gaussian Splatting with Local Depth and Semantic Regularization

See In Detail: Enhancing Sparse-view 3D Gaussian Splatting w...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zongqi He Zhe Xiao Kin-Chung Chan Yushen Zuo Jun Xiao Kin-Man Lam Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D Gaussian Splatting (3DGS) has shown remarkable performance in novel view synthesis. However, its rendering quality deteriorates with sparse inphut views, leading to distorted content and reduced details. This limitation hinders its practical application. To address this issue, we propose a sparse-view 3DGS method. Given the inherently ill-posed nature of sparse-view rendering, incorporating prior information is crucial. We propose a semantic regularization technique, using features extracted from the pretrained DINO-ViT model, to ensure multi-view semantic consistency. Additionally, we propose local depth regularization, which constrains depth values to improve generalization on unseen views. Our method outperforms state-of-the-art novel view synthesis approaches, achieving up to 0.4dB improvement in terms of PSNR on the LLFF dataset, with reduced distortion and enhanced visual quality.

关键词： visualization Solid modeling Acoustic distortion Three-dimensional displays Semantics Coherence Rendering (computer graphics) Feature extraction Acoustics Speech processing

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Enhancing Underwater Images with Color Correction and UW-Net Technology

Enhancing Underwater Images with Color Correction and UW-Net...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Hariharasudhan Chandrasekaran Pooja Bhatt Vinita Tiwari Shailesh Rastogi Department of Civil Engineering Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Vadodara India Balaji Institute of International Business (BIIB) Pune India Department of Finance Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The profound importance of effective underwater image restoration is well-recognized across a variety of domains including underwater exploration, marine biology, environmental monitoring, and autonomous underwater vehicle operations. Current methodologies, however, struggle to adequately address the inherent challenges of underwater imaging, such as severe color distortion and poor visibility due to light absorption and scattering. This paper introduces UW-Net, an innovative end-to-end neural network model specifically tailored for underwater image restoration. UW-Net ingeniously integrates discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) to enhance the extraction and reconstruction of underwater image features. A pivotal aspect of UW-Net is its novel color correction strategy which specifically targets the red and blue spectral distortions, typical of underwater imaging. Additionally, the network incorporates a U-Net architecture with DWT for effective down-sampling and IDWT for precise up-sampling, alongside a chromatic adaptation transform layer aimed at boosting image contrast and color fidelity. Extensive training and evaluation on prominent datasets reveal that UW-Net significantly outperforms existing methods, offering substantial improvements in various performance metrics. This paper delineates the design, implementation, and testing of UW-Net, showcasing its superior capability to restore high-quality visuals from degraded underwater images.

关键词： visualization Image color analysis Neural networks imaging Transforms Distortion Feature extraction Discrete wavelet transforms Image restoration Environmental monitoring

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Advanced Breast Cancer Classification Using a Multi-Branch Spectral Channel Attention Network in Histopathology imaging

Advanced Breast Cancer Classification Using a Multi-Branch S...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Shunmuga Sundari. M Mitul Patel Adusupalle Muni Raju Shailesh Rastogi Department of Artificial Intelligence and Data Science Chennai Institute of Technology Kundrathur Chennai India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Parul University Vadodara India Balaji Institute of International BIIB) Pune India Department of Finance Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In the field of medical image analysis, deep learning has revolutionized the diagnosis and classification of diseases, notably breast cancer. Current methods predominantly utilize convolutional neural networks (CNNs) to analyze spatial features within histopathology images. Despite their efficacy, these methods often overlook the rich frequency domain information that texture-based images inherently possess. Recognizing this gap, our research introduces the Multi-Branch Spectral Channel Attention Network (MbsCANet), a novel architecture designed to enhance breast cancer classification by integrating frequency domain analysis with spatial domain insights. This approach leverages a dual-channel mechanism that combines low and high-frequency features extracted through the two-dimensional discrete cosine transform (DCT). By doing so, MbsCANet not only preserves phase information but also enriches the contextual data crucial for accurate classification. We rigorously tested our model using the BreakHis dataset, achieving unprecedented accuracy levels of 99.01% at image-level and 98.87% at patient-level classification. These results significantly outperform traditional spatial-domain-focused models. The visualization of the model's processing layers further validates its effectiveness, presenting a compelling case for the integration of spectral analysis in medical imaging diagnostics.

关键词： Accuracy Image analysis Histopathology Frequency-domain analysis computer architecture Feature extraction Breast cancer Convolutional neural networks Discrete cosine transforms Medical diagnostic imaging

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Algorithm Optimization and Immersion Enhancement in Virtual Reality Film

Algorithm Optimization and Immersion Enhancement in Virtual ...

引用

Intelligent Systems and Computational Networks (ICISCN), international conference on

作者： Chulei Zhang Hongxing Qian Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

With the widespread application of virtual reality (VR) technology in the fields of entertainment and education, the immersion experienced by users in VR movies and TV shows has become a key factor affecting their acceptance and satisfaction. However, there are still significant shortcomings in terms of image rendering speed in current VR movies and TV shows. This article introduces an algorithm optimization framework based on Convolutional Neural Networks (CNN). Firstly, this article uses CNN algorithm to optimize the image rendering process. Next, use Dynamic Resolution Adjustment (DRS) technology to improve rendering speed and maintain frame rate stability; Then, combining high dynamic range (HDR) rendering and temporal anti aliasing (TAA) technology to enhance the color expression and visual smoothness of the image; Finally, optimize the sensor data processing flow and data transmission protocol. Through experimental evaluation in VR scenes of different complexities, the results showed that the optimized system improved rendering speed by an average of about 58.3%, reduced interaction latency by about 33.3%, and improved image quality indicators PSNR and SSIM by about 26.2% and 9%, respectively. In the above data conclusions, CNN based optimization methods have shown significant advantages in improving VR film and television performance and user experience, providing strong support for the future development of VR film and television technology.

关键词： Image quality visualization TV Heuristic algorithms Virtual reality Motion pictures Rendering (computer graphics) User experience Convolutional neural networks Optimization

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IASTED international conference on computer graphics and imaging

IASTED International Conference on Computer Graphics and Ima...

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Sixth IASTED international conference on computer graphics and imaging

ISBN: (纸本)0889863768

The proceedings contains 61 papers from the IASTED international conference on computer graphics and imaging. Topics discussed include: fuzzy-logical implementation of co-occurrence rules for combining AUS;human animation via hierarchical feature learning of motion data;augmented reality for viewing articulated structures;geometric refinement algorithms in collision-based electric sculpting;three-dimensional rendering of fractal clouds;fingerprint binarization using convex threshold;object classification and tracking in traffic scenes;and a kind of video image compression algorithm based on bivariable fractal function space.

关键词： Animation

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Revolutionizing Tuberculosis Detection: Advanced TBNet Classifier for Chest X-Rays

Revolutionizing Tuberculosis Detection: Advanced TBNet Class...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Nuzhat Noor Islam Prova Sandeep Keshetti Lakshman Kumar Jamili Asmaul Hassan Vishal Narender Punjabi Vishnu Ravi Pace University Seidenberg School of CSIS One Pace Plaza New York City NY USA Amazon Austin TX McKesson Prosper Texas Department of Information Technology Western Governors University Millcreek UT Golden Gate University San Francisco CA USA

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Despite marked improvements in treatment over the decades, tuberculosis (TB) remains a prominent global health issue burdening physicians and society alike, and characterized by a need for accurate and rapid diagnosis methods to control its geographical spread and increase patient efficacy. In this study, we introduce Advanced TBNet Classifier, a deep learning-based model that reliably and very accurately categorizes tuberculosis images. The model uses robust CNN architectures to extract in-formative features with the support of preprocessing methods to attain better image quality for possible better model performance. This work leverages LIME to ensure openness in the diagnostic process through visualizations of interpretable components of the input images explaining model predictions. The interpretability provides healthcare providers insight into the decision-making process, which in turn builds trust. The Advanced TBNet Clas-sifier demonstrates exceptional performance measures with an accuracy, precision, recall and, f1-score that highly aligned with the contribution to this study and is of 99.57%, along with AUC of 99.99%. The accuracy of these findings as a replaceable TB detection tool for doctors in resource-constrained environments is highlighted and diagnostic delays are reduced.

关键词： Deep learning visualization Accuracy Tuberculosis Medical services Predictive models Feature extraction Artificial intelligence X-ray imaging Biomedical imaging

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays

Explainable AI-based Detection and Interpretation of Abnorma...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： Sai Smrithi Muthukumar Bhavika Gandham Trisha Vijayekkumaran Jyotsna C Aiswariya Milan K Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes.

关键词： Measurement Adaptation models visualization Accuracy Explainable AI X-rays Reliability X-ray imaging Medical diagnostic imaging Image classification

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Hyperspectral Image Analysis Using Maximum Abundance Classification

Hyperspectral Image Analysis Using Maximum Abundance Classif...

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international conference on Intelligent Control and Information Processing (ICICIP)

作者： Nian Zhang Sougre-Nonma Stephanie Rouamba Wagdy H. Mahmoud Lara A. Thompson Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA Department of Mechanical Engineering Biomedical Engineering Program University of the District of Columbia Washington D.C. USA

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

This paper presents a satellite hyperspectral image processing method that utilizes a maximum abundance classifier to categorize different regions of hyperspectral images into ground truth classes. First, the class names for each endmember and their corresponding columns in the signature matrix are identified, followed by the visualization of their spectral profiles. Abundance maps for the endmembers are then generated using the fully constrained least squares (FCLS) method. Afterward, the maximum abundance classifier is applied, and the resulting classified image is displayed with color-coded pixels. The abundance maps illustrate the spatial distribution of endmembers across the hyperspectral image, where the abundance values of each pixel represent the proportion of each endmember present. By determining the highest abundance value for each pixel and assigning it to the corresponding endmember class, the pixels within the hyperspectral images are classified. Experimental results demonstrate that the proposed MAC method effectively handles mixed pixels. In addition, it can effectively deal with the mixed pixel problem in hyperspectral images because it identifies components by calculating the abundance values for each pixel rather than relying solely on single spectral features.

关键词： visualization Image segmentation Satellites Graphical models Information processing Hyperspectral imaging Intelligent control Image classification Distribution functions

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Explainable AI-Driven Heart Disease Prediction

Explainable AI-Driven Heart Disease Prediction

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Visual Analytics and Data visualization (ICVADV), international conference on

作者： R Bhuvaneswari P Kumar S Kaviya Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The earliest detection is crucial both to prevention and treatment of heart disease, which ranks highly in the list of worldwide health concerns. Data gathering and processing are performed as the initial phase through several health indicators such as blood pressure, cholesterol, and lifestyle factors. All the inputs are processed and computed with the chances of suffering heart disease using a state-of-art model in machine learning. From, this the best performed machine learning model are integrated in the proposed methodology which results in the better accuracy with 83.9%. For the second step, with the help of explainable AI approaches, relevant elements for each prediction of the model are highlighted. Since the performance of the model is assessed in terms of the interpretation and accuracy of its predictions, the third phase comes as the final one, to deliver medical practitioners with an accurate tool that can be put to use for an early diagnosis and helpful insight. The main objective of our approach is to make EAI models in the health care sector more transparent and user-friendly. LIME (Local Interpretable Model-agnostic Explanations) is a popular method in Explainable Artificial Intelligence (EAI) that has been utilized in the proposed work since it interprets complex machine learning models by highlighting the most significant features contributing to a prediction. In the proposed method, LIME provides insights into the features influencing the model's output, such as whether a patient is likely to have heart disease along with the features which contribute to that prediction.

关键词： Heart Accuracy Explainable AI Computational modeling Visual analytics Medical services Predictive models Medical diagnostic imaging Tuning Diseases

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Learning Two-factor Representation for Magnetic Resonance Image Super-resolution

Learning Two-factor Representation for Magnetic Resonance Im...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Weifeng Wei Heng Chen Pengxiang Su School of Information Engineering Nanchang University Nanchang China School of Mathematics and Computer Science Nanchang University Nanchang China School of Software Nanchang University Nanchang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Magnetic Resonance imaging (MRI) requires a trade-off between resolution, signal-to-noise ratio, and scan time, making high-resolution (HR) acquisition challenging. Therefore, super-resolution for MR image is a feasible solution. However, most existing methods face challenges in accurately learning a continuous volumetric representation from low-resolution image or require HR image for supervision. To solve these challenges, we propose a novel method for MR image super-resolution based on two-factor representation. Specifically, we factorize intensity signals into a linear combination of learnable basis and coefficient factors, enabling efficient continuous volumetric representation from low-resolution MR image. Besides, we introduce a coordinate-based encoding to capture structural relationships between sparse voxels, facilitating smooth completion in unobserved regions. Experiments on BraTS 2019 and MSSEG 2016 datasets demonstrate that our method achieves state-of-the-art performance, providing superior visual fidelity and robustness, particularly in large up-sampling scale MR image super-resolution.

关键词： visualization Image coding Magnetic resonance imaging Superresolution Magnetic resonance Encoding Robustness Speech processing Signal resolution Signal to noise ratio

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