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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901-910 订阅

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Research on Complementary computer Vision Technology Based on Spectral Information Convolutional Neural Networks and Visual Transformers

Research on Complementary Computer Vision Technology Based o...

引用

Electrical, Electronic Information and Communication Engineering (EEICE), international conference on

作者： Jinqiu Pan Peng Chen Kai Liu Wenhao Zhang Yishui County Center for Anti-Corruption and Integrity Education Yishui China School of Physics and Technology University of Jinan Jinan China Yishui County Human Resources and Social Security Bureau Yishui China Linyi University Linyi China

ISBN: (数字)9798331532598

ISBN: (纸本)9798331532604

With the rapid computer vision technology, it plays an important role in modern agriculture, medical diagnosis, environmental monitoring and other fields. However, the computer vision technology mainly relies on RGB image information and ignores the spectral information of objects, which may lead to incomplete information or inaccurate recognition in some applications. Spectral sensors are capable of capturing spectral information of target objects, which provide richer data support for computer vision tasks; meanwhile, Convolutional Neural Networks and vision transformers have achieved remarkable results in image feature extraction and global information capture, and this paper proposes a research idea on complementary computer vision techniques based on spectral sensors, Convolutional Neural Networks and vision transformers, aiming to improve the performance and efficiency of computer vision tasks by combining the spectral information capture capability of spectral sensors with the advantages of Convolutional Neural Networks and vision transformers in image feature extraction and global information capture.

关键词： Image sensors computer vision visualization Image recognition Accuracy Transformers Feature extraction Sensors Convolutional neural networks Medical diagnostic imaging

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Enhancing Breast Cancer Detection Using A Transformer-Based Model

Enhancing Breast Cancer Detection Using A Transformer-Based ...

引用

international conference on Information Science and Communications Technologies (ICISCT)

作者： Shubhi Sharma Yeshwant Singh School of Computer Science UPES Dehradun India

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

Breast cancer is a very common and one of the leading causes of mortality and illness worldwide. Early detection of breast cancer is critical, increasing the chances of long-term survival and successful treatment. Mammography is a primary method for early detection and a widely used imaging technique employing low-dose X-rays. Interpreting mammograms remains challenging due to the complex nature of malignancy anomalies. This advanced imaging technique has revolutionized the screening of breast cancer by facilitating the better visualization of breast tissues. The main challenge that radiologists face in reading mammographic patterns is too many false positives and negatives, which leads to unnecessary biopsies, anxiety among patients, and missing the correct diagnosis. In current times, artificial intelligence (AI) has shown significant success in the medical imaging field. In particular, deep learning (DL) models have outperformed traditional image processing techniques. Allowing accurate and automated predictions, these models are trained on massive datasets to understand the different characteristics of tumors. In this work, a different DL-based model is deployed and developed to detect breast cancer using mammogram images. Also, several DL approaches are discussed, mentioning their benefits and limitations concerning mammography. This work thoroughly analyzes the literature and uses techniques and different evaluation methods to analyze the model's performance across different datasets.

关键词： Deep learning Analytical models visualization Biological system modeling X-rays Transformers Breast cancer Mammography Artificial intelligence Tumors

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ResNet-Based Deep Learning Framework for Liver Cancer Detection with Explainable AI (XAI) Technique

ResNet-Based Deep Learning Framework for Liver Cancer Detect...

引用

international conference on Computing and Networking Technology (ICCNT)

作者： R Aswin H Arjun Kumar U Rahul Varma Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper presents an innovative method for liver tumor detection using ResNet, a deep learning architecture, with medical images as the primary data source. Following tumor detection, the methodology incorporates eXplainable AI (XAI) techniques, particularly LIME(Local Interpretable Model-agnostic Explanations), to give explanations of the model’s decision-making process. This enhances the trustworthiness of the model’s outcomes and improves its applicability in clinical settings. The proposed method reaches state-of-the-art performance in liver tumor detection and provides valuable explanations, rendering it a promising tool for medical professionals in diagnosing liver diseases.

关键词： Deep learning Training Liver cancer Analytical models Accuracy Explainable AI Soft sensors Rendering (computer graphics) Medical diagnostic imaging Tumors

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Medicine recognition from coversheet using VGG16 model

Medicine recognition from coversheet using VGG16 model

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North Karnataka Subsection Flagship international conference (NKCon), IEEE

作者： Pujitha Basamsetty Samitha Kagitha Prathyusha Nulu Vasanthi Pinniboyina Department of Electronics & Communication Engineering Velagapudi Ramakrishna Siddhartha Engineering College Kanuru India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

The use of image processing for medication identification has various advantages, including the extension of medical knowledge beyond formal education. Individuals of all educational backgrounds can quickly identify pharmaceuticals by simply snapping photographs of packing sheets. This method produces quick and accurate results, considerably reducing the time spent looking for pharmaceutical information. This inclusive design creates a safer setting for medicine administration, enhancing patient safety and well-being. The widespread use of tablets in healthcare settings needs effective ways of diagnosing diseases based on tablet cover sheet graphics. This research describes a novel strategy that uses deep learning techniques, notably the visual geometry group 16 (VGG16), a combination of convolutional neural network architecture, to categorize diseases based on cover sheet photos effectively. This model has reached an impressive 97% accuracy, indicating its efficacy in automated diagnosing a medical condition.

关键词： Drugs Geometry Deep learning visualization Accuracy Optical character recognition Safety Reliability Medical diagnostic imaging Diseases

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Enhancing Creative Design: Integrating Artificial Intelligence into Interior Design Education at the University of Sharjah

Enhancing Creative Design: Integrating Artificial Intelligen...

引用

visualization, Informatics and Technology conference (IVIT), international

作者： A. Samad M. Izani A. Razak M. Rosli College of Fine Arts and Design University of Sharjah United Arab Emirates Applied Media Department Higher Colleges of Technology United Arab Emirates Faculty of Creative Multimedia Multimedia University Malaysia University Kuala Lumpur Malaysia

ISBN: (数字)9798350354447

ISBN: (纸本)9798350354454

In an era marked by the transformative impact of Artificial Intelligence (AI) across industries, its integration into interior design presents both challenges and opportunities. This study focuses on exploring the perceptions and preparedness of interior design students towards AI technologies, with a specific focus on students at the University of Sharjah. Through a comprehensive survey and analysis, the research aims to unravel students' awareness, attitudes, and readiness to incorporate AI into their future professional practices. The findings reveal a significant gap between the rapid technological advancements in AI and the current educational approaches, highlighting the need for curriculum enhancements to better align with the evolving technological landscape. Moreover, the study offers valuable implications for the industry, suggesting directions for developing AI tools that resonate more effectively with the needs and expectations of emerging professionals. By bridging this knowledge gap, the research contributes to a more informed and proactive approach in integrating AI within the realm of interior design education and practice, ultimately fostering a generation of professionals well-equipped for the digital future.

关键词： Industries Training Surveys Technological innovation Ethics Rendering (computer graphics) Market research Stakeholders Artificial intelligence Informatics

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Interactive Interior Design Using Augmented Reality and 3D Modeling

Interactive Interior Design Using Augmented Reality and 3D M...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), international conference on

作者： Sharika T R Aleena Santhosh Amrita Ajith Aswathi Gopi Department of Computer Science and Engineering Adi Shankara Institute of Engineering and Technology Kalady India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The project focus on incorporating augmented reality (AR) technology into the field of interior design, with a specific focus on transforming the visualization and experiential aspects of the design process. AR has become a influential tool, fundamentally changing how designers conceive, convey, and bring to life interior spaces. Designers utilize AR tools to showcase furniture, finishes, spatial arrangements, and décor directly within a client's physical environment, providing an immersive preview of design concepts. This initiative improves collaboration between designers and clients, enabling increased engagement and comprehension. Clients actively participate in the design process, gaining the ability to visualize and interact with proposed changes, leading to well-informed decision-making and a stronger sense of ownership over the final design. Designers can rapidly explore multiple design options, optimizing time and resources while better accommodating client preferences. In this project, augmented reality serves as a crucial tool in interior design, reshaping the entire design process, elevating client engagement, and introducing innovative ways to visualize and personalize interior spaces. When using Ground Plane, Vuforia Engine's VISLAM system will attempt to estimate the distance between the camera and the Ground Plane. Visual-Inertial Simultaneous Localization and Mapping (VISLAM) is an algorithm implemented by Vuforia combining the benefits of Visual-Inertial Odometry (VIO) and Simultaneous Localization and Mapping (SLAM).

关键词： visualization Simultaneous localization and mapping Three-dimensional displays Decision making Color Rendering (computer graphics) Real-time systems Odometry Internet of Things Augmented reality

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MIF-SWT-TVM: A Hybrid Multi-Modal Medical Image Fusion Using Stationary Wavelet Transform and Gradient-Based Total Variation Minimization

MIF-SWT-TVM: A Hybrid Multi-Modal Medical Image Fusion Using...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

The Multi-Modal Medical Image Fusion (M3IF) plays a critical task in combining corresponding important information from different imaging modalities (e.g., Magnetic Resonance imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET) etc. to improve clinical diagnosis and decision-making. This paper proposes a hybrid image fusion technique (MIF-SWT- TVM) that leverages Stationary Wavelet Transform (SWT) and Total Variation (TV) Minimization to enhance the visual quality and diagnostic accuracy of fused medical images. The method fuses approximation coefficients (ACs) using an averaging strategy and detail coefficients (DCs) using an absolute maximum selection strategy. The TV regularization is applied post-fusion to reduce noise, suppress artifacts, and enhance structural continuity, leading to a more refined and clearer fused image. The MIF-SWT-TVM is compared with some of the traditional wavelet-based and other fusion techniques, demonstrating superior preservation of anatomical structures and contrast enhancement. The MIF-SWT- TVM effectively enhances diagnostic features, reduces noise, and provides better visual quality compared to existing fusion techniques.

关键词： visualization TV Magnetic resonance imaging Computed tomography Noise Minimization Positron emission tomography Image fusion Standards Principal component analysis

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A Recent Advancements in Multi-Modal Medical Image Fusion Techniques using SWT, NSST, NSCT, and CNN

A Recent Advancements in Multi-Modal Medical Image Fusion Te...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Image fusion is a method used in image processing to provide a more complete representation by amalgamating features and data from many images. Multimodal medical image fusion involves the integration of medical images from many imaging modalities, including computed tomography (CT) scans, positron emission tomography (PET), and magnetic resonance imaging (MRI), into one single dataset. This integration enhances the visualisation of anatomical structures and clinical situations, hence improving diagnostic accuracy by leveraging the strengths of each medium. This study employs MRI, CT, and PET scans as experimental modalities. This review aims to compare various multi modal medical image approach based on Stationary Wavelet Transform (SWT), Non-Subsampled Shearlet Transform (NSST), Convolutional Neural Network (CNN) and NonSubsampled Contourlet Transform (NSCT). This study examines the latest conventional and non-conventional research conducted within these disciplines. It further evaluates these approaches according to diverse image quality metrics and many quantitative assessments. According to this comparison, CNN-based fusion demonstrates superior results, as the overall visual and parametric quality of the fusion outcomes surpasses that of the other approaches evaluated.

关键词： Image quality Wavelet transforms visualization Reviews Magnetic resonance imaging Computed tomography Convolutional neural networks Positron emission tomography Medical diagnostic imaging Image fusion

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Improving Image Super-Resolution Reconstruction with InceptionV3 and Dual Perceptual Loss

Improving Image Super-Resolution Reconstruction with Incepti...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Siriwan Chuengtheerapanit Warawut Kesornsukhon Kanabadee Srisomboon Luepol Pipanmekaporn San Ratanasanya Department of Computer and Information Science King Mongkut’s University of Norh Bangkok Bangkok Thailand Department of Electrical and Computer Engineering King Mongkut’s Universiy of North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This paper proposes a method to improve the quality of image super-resolution (SR) by using the InceptionV3 network and Dual Perceptual Loss (DP Loss) within the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) framework. Reconstruction of high-resolution images is enhanced by the proposed method in terms of both structural integrity and perceptual quality. The proposed method solves the limitations of previous models, including over-sharpening and color inaccuracies, which are prevalent in SR tasks. The proposed method, which replaces the ResNet Model with InceptionV3, outperforms the baseline ESRGAN-DP in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). It achieves up to 0.61% improvement in PSNR, 0.90% in SSIM, and 5.9% in LPIPS across multiple benchmark datasets.

关键词： visualization PSNR Image color analysis Superresolution Benchmark testing Feature extraction Solids Image reconstruction Standards Biomedical imaging

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Dual Fusion Network for Hyperspectral Semantic Segmentation 12th

Dual Fusion Network for Hyperspectral Semantic Segmentation

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12th international conference on Image and graphics, ICIG 2023

作者： Ding, Xuan Gu, Shuo Yang, Jian PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031463075

With the development of imaging technology, it becomes increasingly easy to obtain hyperspectral images (HSI) containing rich spectral information. The application of hyperspectral images in autonomous driving is expected to become a reality. Hyperspectral images have higher dimensions and more information than RGB images, which brings the challenge of training with limited samples. It is not easy to train satisfactory semantic segmentation models directly from hyperspectral images. In this paper, we propose a hyperspectral dual-fusion (hyperDF) network that can fully utilize the pre-training knowledge of the RGB modality to improve the performance of hyperspectral semantic segmentation. Specifically, we generate pseudo-color images from hyperspectral images to simulate RGB signals in order to make better use of the pre-trained RGB semantic segmentation models. The pseudo-color and hyperspectral images are processed in a dual encoder structure and then fused through a channel-wise attention fusion module. Finally, a multi-scale decoder injected with low-level features is used to predict the semantic segmentation results. Experimental results on the Hyperspectral City V2.0 dataset show that our method achieves state-of-the-art results, with a mIoU of 51.68%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantic Segmentation

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