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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91-100 订阅

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Diegetic User Interfaces in Extended Reality for 3D Medical visualization 7

Diegetic User Interfaces in Extended Reality for 3D Medical ...

引用

7th IEEE international conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Cascarano, Pasquale Loretti, Andrea Di Pasquale, Alessio Hajahmadi, Shirin Vallasciani, Giacomo Zanuttini, Luca Martinoni, Matteo Marfia, Gustavo University of Bologna Dept. of the Arts Bologna Italy University of Bologna Dept. of Comp. Science and Eng. Bologna Italy Irccs Inst. of Neur. Sciences Dept. of Neurosurgery Bologna Italy

ISBN: (纸本)9798331521578

The integration of 3D anatomical models derived from DICOM images into extended reality (XR) interfaces significantly enhances the visualization and interaction with anatomical data, particularly in educational, pre-operative, and intra-operative medical scenarios. This paper presents an XR application developed with Unity 3D for the Meta Quest 3, enabling concurrent visualization of DICOM images and interaction with the 3D model in an immersive XR environment. The application incorporates an advanced diegetic user interface, allowing users to manipulate the 3D model intuitively through hand gestures for actions like annotating, dragging, dropping, and scaling objects. Utilizing a virtual tablet and slider, users can precisely control DICOM slice planes that intersect the 3D model, providing real-time updates of visual content. Enhanced by hand tracking, this interface aims to facilitate the natural manipulation of cross-sectional views, thereby optimizing anatomical visualization. © 2025 IEEE.

关键词： Medical imaging

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Augmented Reality for Enhanced Surgical Precision: A Framework for Real-Time visualization and Navigation 1

Augmented Reality for Enhanced Surgical Precision: A Framewo...

引用

1st international conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, Jagbir Vedula, Jagadeesh Ragothaman, Hariharan Muzaffar, Shojonov Yulduz, Urazbaeva Singh, Satendra Pratap India Digital Transformation Lead Meghaz Inc. HoustonTX United States Center of Urgench State University The Information Security department The Digital Education Technologies Uzbekistan Mamun university Pedagogy and psychology Department Khiva Uzbekistan Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331518578

The advancement of Augmented reality (AR) technology has made it possible to use it in surgical precision and better the lives of patients. This paper outlines a real-time visualization and navigation framework where AR is integrated into surgical work. CT and MRI imaging data are used in the system to create accurate and rather detailed preoperative 3D anatomical models. These models are overlaid on the surgeon's vision, being the integral part of the system, providing the surgeon with dynamic constant assistance during procedures. To surmount the problem of misalignment of the virtual overlays with the patient's anatomy due to movements of the patient in the operating room, the framework uses complex tracking algorithms as well as machine learning techniques. It highlights planner pre-operative, intra-operative visualization and decision support mainly in complicated surgical cases. In the case of minimally invasive surgery, the framework was used to position the instruments correctly with improved accuracy, cutting short on time to operate, and countering complications. This research shows how AR can benefit today's surgery, as an origination of increased accuracy and a closing of the gap between conception and operations. It has also discussed some of the issues like scalability, cost, and the required training for the surgeon for the use of such system and has therefore provided a direction for the large-scale use of AR-assisted surgical systems. © 2025 IEEE.

关键词： Augmented reality

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Combining Stable Diffusion and Object Detection for Semi-automatic Environment Decoration 41st

Combining Stable Diffusion and Object Detection for Semi-a...

引用

41st computer graphics international conference, CGI 2024

作者： Drescher, Philipp Ruprecht, Irena Settgast, Volker Computer Graphics and Knowledge Visualization Graz University of Technology Graz Austria Fraunhofer Austria Research GmbH Graz Austria

ISBN: (纸本)9783031820236

Enhancing immersive environments in an efficient way is essential across various industries. However, the task of selecting and placing suitable objects to decorate 3D scenes often requires a substantial investment of time and expertise. We present a novel semi-automatic approach to accelerate this process by fusing state-of-the-art generative text-to-image models with object detection. We use a rendering of a plain 3D scene as input for Stable Diffusion and generate an image depicting a decorated version. With object detection, we identify the location, size and class of decorative elements in the generated image. Based on this information, we populate the original 3D scene with matching assets from a database. Our results demonstrate the capabilities of our proposed approach in enhancing 3D scenes through the incorporation of decorative models across different scenarios. We show that our method seamlessly integrates into existing 3D content creation tools highlighting its applicability within established workflows. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Architectural design

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Segmentation and Immersive visualization of Brain Lesions Using Deep Learning and Virtual Reality 19

Segmentation and Immersive Visualization of Brain Lesions Us...

引用

19th ACM SIGGRAPH international conference on Virtual-Reality Continuum and its Applications in Industry, VRCAI 2024

作者： Kelley, Brendan Plabst, Lucas Plabst, Lena Natural User Interaction Lab Colorado State University Fort CollinsCO United States Department of Human-Computer Interaction Julius Maximilian University of Würzburg Würzburg Germany

ISBN: (纸本)9798400713484

Magnetic resonance imaging (MRIs) are commonly used for diagnosing potential neurological disorders, however preparation and interpretation of MRI scans requires professional oversight. Additionally, MRIs are typically viewed as single cross sections of the affected regions which does not always capture the full picture of brain lesions and can be difficult to understand due to 2D's inherent abstraction of our 3D world. To address these challenges we propose a immersive visualization pipeline that combines deep learning image segmentation techniques using a VGG-16 model trained on MRI fluid attenuated inversion recovery (FLAIR) with virtual reality (VR) immersive analytics. Our visualization pipeline begins with our VGG-16 model predicting which regions of the brain are potentially affected by a disease. This output, along with the original scan, are then volumentrically rendered. These renders can then be viewed in VR using an head mounted display (HMD). Within the HMD users can move through the volumentric renderings to view the affected regions and utilize planes to view cross sections of the MRI scans. Our work provides a potential pipeline and tool for diagnosis and care. © 2024 ACM.

关键词： Image segmentation

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Customized virtual liver tumor resection surgery simulation 16

Customized virtual liver tumor resection surgery simulation

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2024 16th international conference on graphics and Image Processing, ICGIP 2024

作者： Wang, Ning Jia, Shiyu Zhang, Jingli Xiu, Wenli College of Computer Science and Technology Qingdao University Qingdao266071 China Department of Pediatric Surgery Affiliated Hospital of Qingdao University Qingdao University Shandong Qingdao266003 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Due to variations in the size, shape, and location of liver tumors among individuals, surgical plans for liver tumor resection differ significantly. Traditional simulators use relatively uniform models in terms of size, position, and shape, which cannot replicate the patient's true anatomical structure. Through 3D reconstruction technology, a customized liver simulation scenario can be quickly established for each patient using a 3D surface model reconstructed from their medical imaging data. This scenario allows for the simulation of different surgical plans. Initially, a 3D surface model is reconstructed based on the actual medical imaging data of each patient's liver. Since 3D reconstruction technology cannot reconstruct the ligaments of the liver, virtual ligaments and virtual ligament envelopes are reconstructed on the liver surface according to its anatomical structure. Next, these components are assembled in a volume model generator, where boundary conditions are set on the virtual ligament envelopes to generate the volume model scenario. Finally, a simulator is used to perform surgical simulations on the patient's customized volume model scenario. Simulation results indicate that the customized virtual liver scenario can produce realistic deformation, collision, and cutting effects. The simulation frame rate meets real-time requirements, allowing surgical simulations based on the patient's pathological characteristics, thereby greatly simulating real surgery. © 2025 SPIE.

关键词： Tumors

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Fuzzy-AttnNet: A Deep Learning Framework for Image Retrieval Using Fuzzy Feature Fusion 16

Fuzzy-AttnNet: A Deep Learning Framework for Image Retrieval...

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2024 16th international conference on graphics and Image Processing, ICGIP 2024

作者： Liu, Shengyu Pan, Jingshan Li, Na Hu, Tao Tian, Tangbin Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Image retrieval is the process of searching for similar images based on their visual content, which has significant applications in the field of medical imaging. Quickly retrieving similar images from a medical image database can assist doctors in rapid browsing and diagnosis. Although feature fusion-based descriptors have achieved great success in the field of image retrieval, there are relatively few cases of using fusion feature descriptors for image retrieval in the medical imaging field. To address this gap, we propose a novel feature extraction model, Fuzzy-AttnNet, aimed at enhancing the performance of medical image retrieval using fusion descriptors. The model simulates dilated convolutions and self-attention mechanisms, comprehensively modeling both local and global features of medical images and performing feature fusion. The fused descriptors are then applied to image retrieval. Additionally, Fuzzy-AttnNet employs a parallel optimization strategy using Arcface loss and Softmax loss, which increases intra-class spacing and significantly improves the model's recognition performance across different categories. In subclass classification tasks, Fuzzy-AttnNet demonstrates significant advantages, with an average precision improvement of approximately 10% and a recall increase of about 30%. The experimental results indicate that our proposed feature extraction model significantly enhances the accuracy of medical image retrieval. © 2025 SPIE.

关键词： Medical imaging

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Application of computer graphics and BIM in Three-Dimensional visualization and Virtual Reality of Water Conservancy Buildings

Application of Computer Graphics and BIM in Three-Dimensiona...

引用

Electronics, Energy Systems and Power Engineering (EESPE), IEEE international conference on

作者： Zekun Chang Shandong Vocational College of Industry Zibo China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper combines computer graphics and building information modeling (BIM) technology to propose an innovative three-dimensional visualization optimization algorithm for water conservancy buildings. The algorithm combines BIM data with efficient rendering technology in graphics, and proposes a method based on multi-level model generation and detail processing optimization to improve the rendering speed and accuracy of three-dimensional models. In the experimental part, a BIM model of a large water conservancy project was used for testing. The results show that compared with the traditional rendering method, the algorithm has improved the rendering accuracy by 18.5% and the rendering speed by 30%. In practical applications, this algorithm can effectively reduce the rendering time while ensuring the details and accuracy of the model, providing more accurate and efficient visualization support for water conservancy construction projects. In addition, the visualization effect is displayed through a virtual reality platform, which further verifies the superior performance of the algorithm in a virtual reality environment. The results of this study show that the innovative algorithm based on BIM and computer graphics cannot only improve the design and construction efficiency of water conservancy buildings, but also optimize the engineering management process, and has strong application value and promotion prospects.

关键词： Solid modeling Accuracy Three-dimensional displays Computational modeling Buildings Virtual reality Water conservation Rendering (computer graphics) Optimization Water resources

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Point Cloud Reconstruction and Deformation Based on Point Primitives 16

Point Cloud Reconstruction and Deformation Based on Point Pr...

引用

2024 16th international conference on graphics and Image Processing, ICGIP 2024

作者： Wang, Le Li, Xiaoli Du, Zhenlong School of Computer Science & Technology Nanjing TECH University Nanjing211816 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

With the rapid development of 3D point cloud data acquisition technology, its application in fields such as virtual reality, augmented reality, medical imaging analysis, and robotics is becoming increasingly widespread. Although current research mainly focuses on implicit space point cloud processing, an increasing amount of evidence suggests that explicit space methods, especially those based on point data primitives, can more effectively achieve neural surface reconstruction and point cloud deformation. This study completes reconstruction by imposing constraints in point space, normal space, and image space, and use isometric constraints to construct a point cloud deformation field for precise deformation of point cloud models. Experiments on the Studio dataset and the Stanford 3D Scanning Repository demonstrated good neural surface reconstruction effects. Compared to the Instant NGP method, the reconstruction loss was reduced by 13.9% and 27.7%, respectively, and effectively avoided reconstruction loopholes. Ablation experiments further confirmed the role of AIAP constraints in improving the quality of point cloud deformation, with optimization effects reaching 7.4%, respectively. The point cloud reconstruction and deformation method based on point primitives proposed in this paper, from explicit semantic space constraints and guided learning processes, not only completes the reconstruction and deformation of point clouds with high fidelity but also shows strong robustness and generalization. © 2025 SPIE.

关键词： Medical robotics

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Hyperspectral image methane detection based on DETR 16

Hyperspectral image methane detection based on DETR

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2024 16th international conference on graphics and Image Processing, ICGIP 2024

作者： Liu, Qiwen Li, Xiaoli Du, Zhenlong School of Computer Science and Technology Nanjing TECH University Nanjing211816 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

As a potent greenhouse gas, methane (CH4) leakage not only intensifies global warming, but also brings huge security risks. In recent years, with the rapid development of remote sensing technology, methane leakage detection using hyperspectral images has become a research hotspot. Among them, the airborne Visible Infrared imaging Spectrometer (AVIRIS-NG) shows great potential in methane leakage detection with its high spectral resolution and wide spectral coverage. However, due to the complexity of land type conditions and the interference of confounding factors similar to the spectral characteristics of methane, the existing analytical methods are often difficult to obtain ideal detection results. Therefore, based on the DETR model, a novel end-to-end spectral absorption wavelength sensing transformer target detection network is proposed to improve the accuracy and efficiency of methane gas leakage detection. The reference spectrum feature generator and query refiner modules together improve the design of traditional transformer. Based on the traditional linear filter, a new spectral linear filter is proposed, which can better whiten the background distribution and amplify the methane signal by strategically selecting the relevant pixels in the spectral domain. The method presented in this paper has certain significance for improving the detection of methane gas leakage. © 2025 SPIE.

关键词： Remote sensing

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Prototype Transferred Diverse Features for Day-night Cross-domain Facial Expression Recognition 16

Prototype Transferred Diverse Features for Day-night Cross-d...

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2024 16th international conference on graphics and Image Processing, ICGIP 2024

作者： Ma, Teng Qi, Yuhan Wang, ZhuoRan Li, YiPei Wang, Jiaxiang State Grid Corporation of Shanxi Province Shanxi 030025 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Beijing Institute of Technology China Dublin International College Ireland

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Facial Expression Recognition (FER) encounters significant challenges due to the limited sensitivity of visible light images in low light conditions. Most existing cross-domain emotion recognition studies have focused on domain adaptation for different visible light datasets, often neglecting the problem of emotion recognition under varying lighting conditions. To address this, we introduce the first cross-domain emotion recognition study for day and night environments, emphasizing the modal migration between normal visible light and low-light emotion maps. Considering the limited number of emotion maps and the reduced emotional detail in nighttime imaging, we designed an attention-switching diversified feature capture module to focus on facial expressions and extract more local emotional features that aid in migration. Given the significant domain shifts between samples in different lighting environments, we further developed a prototype feature transfer module to learn modality-independent category features and reduce domain differences between visible and low-light features. To tackle the challenge of numerous low sentiment value samples in the dataset, we introduce a high-confidence blending module to filter informative visible and infrared samples for fusion, producing features that combine the styles of both domains. Compared to state-of-the-art methods in domain adaptation and cross-domain emotion recognition, our approach demonstrates superior performance and validates its effectiveness. © 2025 SPIE.

关键词： Emotion Recognition

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