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

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Semiautomatic Generation of a Three-Dimensional Human Anatomical Model of Bone for Biomedical Applications: First Approach 46th

Semiautomatic Generation of a Three-Dimensional Human Anat...

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46th Mexican conference on Biomedical Engineering, CNIB 2023

作者： Alavez, Nohemi Barranco Hurtado-Esquivel, Christian Isaac Trujillo-Romero, Citlalli Jessica Ríos, Álvaro Anzueto Laboratorio de Biomecánica Departamento de Ingeniería Biónica Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas-IPN Avenida Instituto Politécnico Nacional No. 2580 Barrio la Laguna Ticomán Ciudad de México07340 Mexico División de Investigación en Ingeniería Médica Instituto Nacional de Rehabilitación-LGII Calz. México-Xochimilco 289 Coapa Guadalupe Tlalpan Tlalpan Ciudad de México14389 Mexico

ISBN: (纸本)9783031469329

Third-dimensional human anatomical models have been used to better understand the human body. However, one of their most important uses is the personalization of any type of treatment. 3D human models of bones from the lower extremities are crucial for visualization, treatment planning, and orthotic and prosthetic design. Nonetheless, this 3D models must be compatibles with any modeling software, like those working with the finite element method. Therefore, a methodology to generate 3D anatomical models compatible with CAD systems was proposed. A couple of sets of Computed Tomography (CT) images in DICOM (Digital imaging and Communications in Medicine) format was used. To reduce the computational cost, different optimization algorithms were implemented. A Gaussian low-pass filter was implemented for bone segmentation, and the thresholding method was applied. Morphological operations to eliminate sparse pixels, gaps and to generate smooth edges were performed. The Marching Cubes algorithm was used to generate a three-dimensional mesh. The 3D model scale was adjusted by the metadata in the DICOM files. The 3D model surfaces were smoothed by a Gaussian filter applied to the segmented images and an averaging filter used on the model’s vertices. To verify the dimensions of the reconstructed model, they were compared with those obtained from a model reconstructed in InVesalius 3.1. Approximately, it took 1.95 min to generate the exported 3D model in a CAD-compatible STL file. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Three dimensional computer graphics

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Synergistic Fusion of Multi-Contrast imaging and Computational Image Synthesis for Quadruple Contrast-Enhanced Images and Multi-Map Generation

Synergistic Fusion of Multi-Contrast Imaging and Computation...

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Communication, computer Sciences and Engineering (IC3SE), international conference on

作者： Poonam Mishra Hameed Hassan Khalaf Mohammed Yousif Arabi Taha Raad Al-Shaikhli Applied Mathematics ASET Amity University Raipur Chhattisgarh India Al-Manara College for Medical Sciences ( Maysan Iraq College of Computer National University of Science and Technology Dhi Qar Iraq Department of Medical Laboratories Technology AL-Nisour University College Baghdad Iraq

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Accurate tissue maps and a wide range of imaging modalities are required for medical imaging assessments and therapies. Our exclusive technology employs multi-contrast imaging and cutting-edge computer image synthesis to produce high-quality multi-maps and pictures of body regions and disorders. This approach uses deep learning fusion and computational image synthesis to construct multi-maps of disease markers and tissue features from several imaging modalities. Quality inspection and validation assure the correctness of the images and multi-maps. In terms of memory, accuracy, precision, and other performance measures, our strategy surpassed all other approaches. Reliable data gathering and visualization of physiological structures may enhance patient outcomes and medical imaging technologies. Applying the research results to other clinical contexts necessitates confirming the findings with real clinical data. The technology available might help medical practitioners comprehend and analyse complicated biological systems. There may be personalized treatment regimens and diagnostic imaging.

关键词： Deep learning Accuracy Protocols Image synthesis Medical treatment Physiology Reliability

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Geometrically Consistent Partial Shape Matching

Geometrically Consistent Partial Shape Matching

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

作者： Viktoria Ehm Paul Roetzer Marvin Eisenberger Maolin Gao Florian Bernard Daniel Cremers Technical University of Munich Munich Center for Machine Learning University of Bonn

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

Finding correspondences between 3D shapes is a crucial problem in computer vision and graphics, which is for example relevant for tasks like shape interpolation, pose transfer, or texture transfer. An often neglected but essential property of matchings is geometric consistency, which means that neighboring triangles in one shape are consistently matched to neighboring triangles in the other shape. Moreover, while in practice one often has only access to partial observations of a 3D shape (e.g. due to occlusion, or scanning artifacts), there do not exist any methods that directly address geometrically consistent partial shape matching. In this work we fill this gap by proposing to integrate state-of-the-art deep shape features into a novel integer linear programming partial shape matching formulation. Our optimization yields a globally optimal solution on low resolution shapes, which we then refine using a coarse-to-fine scheme. We show that our method can find more reliable results on partial shapes in comparison to existing geometrically consistent algorithms (for which one first has to fill missing parts with a dummy geometry). Moreover, our matchings are substantially smoother than learning-based state-of-the-art shape matching methods. The code of this work is publicly available at https://***/vikiehm/ geometrically-consistent-partial-shape-matching.

关键词： Geometry Interpolation computer vision Three-dimensional displays Shape Integer linear programming Filling

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A Comprehensive Exploration of Reconstruction-based Computational Frameworks for graphics and visualization

A Comprehensive Exploration of Reconstruction-based Computat...

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Advancement in Technology (ICONAT), international conference for

作者： Madhav Singh Solanki H Srikantha Kalyan Acharjya Department of Computer Science and Engineering Sanskriti University Mathura Uttar Pradesh India Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Reconstruction-primarily based computational frameworks offer a vital basis for photos and visualization. The conventional strategies used to generate visible representations of a given model depend closely upon analytically determined answers for the underlying geometry, making them tough, if no longer impossible, to increase for more complex systems. Therefore, it is beneficial to explore reconstruction-based methods as an alternative for tackling the various challenges associated with photographs and visualization. This paper offers a complete exploration of reconstruction-based computational frameworks for pictures and visualization. Via an intensive survey of different strategies and methods, the merits and downsides of every method are analyzed and compared to each other. Additionally, current literature is mentioned, presenting insights into furthering the development of reconstruction-primarily based methods. Finally, an evaluation of the important thing layout concerns is presented, concluding with destiny directions for research. Those encompass leveraging records-pushed strategies, specializing in robustness, and enabling actual-time overall performance. Normal, this paper provides a deep and well-rounded exploration of the potential of reconstruction-based computational frameworks for photos and visualization. It offers insights into existing literature and the destiny guidelines for studies in addition to a clear review of the one-of-a-kind strategies available. The authors provide a thorough analysis in their conclusions, paving the way for fruitful discussions and doubtlessly main to the development of stepped-forward techniques.

关键词： Surveys Geometry visualization Analytical models Reviews Computational modeling Layout Robustness Complex systems Guidelines

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S4C: Self-Supervised Semantic Scene Completion With Neural Fields

S4C: Self-Supervised Semantic Scene Completion With Neural F...

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

作者： Adrian Hayler Felix Wimbauer Dominik Muhle Christian Rupprecht Daniel Cremers Technical University of Munich MCML University of Oxford

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

3D semantic scene understanding is a fundamental challenge in computer vision. It enables mobile agents to autonomously plan and navigate arbitrary environments. SSC formalizes this challenge as jointly estimating dense geometry and semantic information from sparse observations of a scene. Current methods for SSC are generally trained on 3D ground truth based on aggregated LiDAR scans. This process relies on special sensors and annotation by hand which are costly and do not scale well. To overcome this issue, our work presents the first self-supervised approach to SSC called S4C that does not rely on 3D ground truth data. Our proposed method can reconstruct a scene from a single image and only relies on videos and pseudo segmentation ground truth generated from off-the-shelf image segmentation network during training. Unlike existing methods, which use discrete voxel grids, we represent scenes as implicit semantic fields. This formulation allows querying any point within the camera frustum for occupancy and semantic class. Our architecture is trained through rendering-based self-supervised losses. Nonetheless, our method achieves performance close to fully supervised state-of-the-art methods. Additionally, our method demonstrates strong generalization capabilities and can synthesize accurate segmentation maps for far away viewpoints.

关键词： Training Image segmentation Three-dimensional displays Navigation Semantics Rendering (computer graphics) Sensors

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A Novel Sky-Aware Dehazing Approach for Outdoor Autonomous Systems

A Novel Sky-Aware Dehazing Approach for Outdoor Autonomous S...

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Software Engineering and computer Science (CSECS), international conference on

作者： Xiaoyan Wu Dali Xu Fangfa Fu Bing Yang Hongyang Liu Kanglong Liu Xiyu Ding Chao Wu Na Niu School of Computer and Control Engineering Northeast Forestry University Harbin China School of Astronautics Harbin Institute of Technology Harbin China School of Computer Science Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Clear outdoor images are essential for autonomous driving and accurate target detection, especially in haze. The majority of algorithms are unable to adequately address the issue of dehazing, resulting in a range of distortions, particularly in the sky area. This paper proposes an advanced dehazing algorithm for enhancing sky-area visuals (ESV). We segment the image into sky and non-sky areas, with atmospheric light levels being determined within the sky area. To enhance the recovery of the sky region, we suggest fine-tuning the sky's transmission to a predetermined constant value. Ultimately, the dehazed image is retrieved utilizing the atmospheric scattering model. Extensive experiments have shown that our proposed algorithm outperforms alternative methods, increasing PSNR by up to 1.3733%, 1.6360%, 2.4169%, 0.9512%, SSIM by up to 4.8995%, 0.6281%, 6.5335%, 8.7165%, enhancing the visuals of sky-area, compared to DCP, CAP, HC-CEP and AOD-Net.

关键词： visualization Atmospheric modeling Software algorithms Scattering Reinforcement learning Object detection Distortion Rendering (computer graphics) Robustness Software engineering

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Image Inpainting using YOLOv8 and LaMa Model

Image Inpainting using YOLOv8 and LaMa Model

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Emerging Technology (INCET), international conference for

作者： Shorya Jain Vats Shivam Anupama.P. Bidargaddi Srivatsa Malipatil Kartik Patil Computer Science and Engineering KLE Technological University Hubballi India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Image inpainting, a crucial component of computer vision, addresses challenges in image restoration, content cre-ation, and medical imaging. Traditional methods often struggle with complex scenes, prompting the integration of YOLOv8 for precise object detection and LaMa for generative inpainting. YOLOv8 identifies regions for inpainting, enhancing accuracy and speed. LaMa employs a latent space augmentation technique, generating contextually relevant content. The hybrid approach aims to improve inpainting quality and efficiency. In a rapidly evolving digital landscape, image inpainting finds applications in diverse domains. Our paper advances inpainting using YOLOv8 and LaMa, demonstrating superior accuracy and visual quality. Combining YOLOv8 for masking and LaMa for inpainting this paper has achieved some astonishing results.

关键词： visualization computer vision Accuracy Object detection Image restoration Biomedical imaging

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Advanced Hybrid AI Methods for Brain Tumor Analysis: Detection and Categorization

Advanced Hybrid AI Methods for Brain Tumor Analysis: Detecti...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Narenthirakumar Appavu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

This study uses an MRI, or magnetic resonance imaging a non-invasive method that produces high-contrast greyscale pictures, to accurately classify brain cancers, specifically pituitary, glioma, and meningioma. Convolutional neural networks (CNNs), which offer notable improvements in diagnosis accuracy, have become essential in medical imaging with the emergence of deep learning. This study uses five pre-trained CNN models using transfer learning (TL) on the publically available CE-MRI (Figshare) dataset. For tumor classification, the process entails incorporating bespoke layers, among them fully connected (FC) layers, and initializing ResNets with ImageNet weights. Extensive tests that use data augmentation to improve classification accuracy assess the resilience of these optimized ResNets. Grad-CAM visualization provides interpretation in tumor localization by highlighting the model's focal points. With an Accuracy of 95.72%, Sensitivity of 95.89%, Specificity of 96.98%, Precision of 94.05%, and an F1Score of 95.01%, ResNet50 outperforms the other examined designs in terms of operational efficiency and performance. These findings demonstrate how well this method works to increase the diagnostic precision of brain tumor classification.

关键词： Accuracy Sensitivity Magnetic resonance imaging Transfer learning Brain tumors Data visualization Brain modeling Convolutional neural networks Residual neural networks Testing

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End-To-End Phase Retrieval from Single-Shot Fringe Image for 3D Face Reconstruction 12th

End-To-End Phase Retrieval from Single-Shot Fringe Image for...

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12th international conference on Image and graphics, ICIG 2023

作者： Zhang, Xiao You, Zhi sheng Zhu, Jiangping You, Di Cheng, Peng National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China College of Computer Science Sichuan University Chengdu China School of Aeronautics and Astronautics Sichuan University Chengdu China Chengdu Aeronautic Polytechnic Chengdu China

ISBN: (纸本)9783031463105

Phase retrieval is a critical aspect of structured light projection-based three-dimensional (3D) face imaging. The accurate extraction of desired phase information from a minimal number of fringe images without motion blur distortion remains a major challenge. Deep learning has gained attention in solving various optical measurement tasks, and in this study, we propose a deep learning-based dual-generator neural network (DGNET) that consists of two generators in series. The first generator filters interference caused by invalid regions, while the second converts valid regions in the fringe pattern into the final unwrapped phase. Moreover, the DGNET utilizes a single fringe image as input, which enhances the measurement speed and eliminates the impact of pose changes. Experimental results confirm the accuracy and feasibility of performing a phase-retrieval task from a single fringe pattern using the proposed DGNET. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Interferometry

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Low-latency Visual Previews of Large Synchrotron Micro-CT Datasets

Low-latency Visual Previews of Large Synchrotron Micro-CT Da...

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2023 IEEE international conference on Big Data, BigData 2023

作者： Jerome, Nicholas Tan Chilingaryan, Suren Van De Kamp, Thomas Kopmann, Andreas Institute for Data Processing and Electronics Eggenstein-Leopoldshafen Germany Institute for Photon Science and Synchrotron Radiation Eggenstein-Leopoldshafen Germany Laboratory for Applications of Synchrotron Radiation Karlsruhe Germany

ISBN: (纸本)9798350324457

The unprecedented rate at which synchrotron radiation facilities are producing micro-computed (micro-CT) datasets has resulted in an overwhelming amount of data that scientists struggle to browse and interact with in real-time. Thousands of arthropods are scanned into micro-CT within the NOVA project, producing a large collection of gigabyte-sized datasets. In this work, we present methods to reduce the size of this data, scaling it from gigabytes to megabytes, enabling the micro-CT dataset to be delivered in real-time. In addition, arthropods can be identified by scientists even after implementing data reduction methodologies. Our initial step is to devise three distinct visual previews that comply with the best practices of data exploration. Subsequently, each visual preview warrants its own design consideration, thereby necessitating an individual data processing pipeline for each. We aim to present data reduction algorithms applied across the data processing pipelines. Particularly, we reduce size by using the multi-resolution slicemaps, the server-side rendering, and the histogram filtering approaches. In the evaluation, we examine the disparities of each method to identify the most favorable arrangement for our operation, which can then be adjusted for other experiments that have comparable necessities. Our demonstration proved that reducing the dataset size to the megabyte range is achievable without compromising the arthropod's geometry information. © 2023 IEEE.

关键词： Computed tomography Data reduction Data visualization Real-time systems Rendering (computer graphics) Three-dimensional displays

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