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检索条件"任意字段=27th International Conference on Computer Graphics and Vision, GraphiCon 2017"

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graphicon 2017 - 27th international conference on computer graphics and vision

GraphiCon 2017 - 27th International Conference on Computer G...

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27th international conference on computer graphics and vision, graphicon 2017

ISBN: (纸本)9785794429633

the proceedings contain 74 papers. the topics discussed include: scientific visualization and visual analytics challenges;contemporary information technology: information visualization, virtual environment, visual perception and analisys;paleographic dating of birch bark manuscripts;a concept for database-oriented 3D graphics engine infrastructure;comparison of hierarchies for occlusion culling based on occlusion queries;reconstruction of BSDF based on optimization of microrelief normal distribution;and stochastic ray tracing methods in problems of photorealistic image synthesis for augmented reality systems.

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27th international conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2019 - Proceedings

27th International Conference in Central Europe on Computer ...

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27th international conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2019

ISBN: (纸本)9788086943381

the proceedings contain 11 papers. the topics discussed include: tools for development of interactive web-based maps: application in healthcare;porting a visual inertial SLAM algorithm to android devices;open data visualization in Danish schools: a case study;prototyping a game engine architecture as a multi-agent system;real time pedestrian and object detection and tracking-based deep learning. application to drone visual tracking;title segmentation in Arabic document pages;improving facial attraction in videos;immersive analytics sensemaking on different platforms;and the role of polarization in the multiple reflections modeling.

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27th international conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2019 - Proceedings

27th International Conference in Central Europe on Computer ...

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27th international conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2019

ISBN: (纸本)9788086943374

the proceedings contain 20 papers. the topics discussed include: cubic spline interpolation in real-time applications using three control points;creating digital models of paleontological sample by photogrammetry and computed tomography;deep learning-based overlapping-pigs separation by balancing accuracy and execution time;deep learning-based overlapping-pigs separation by balancing accuracy and execution time;stylized sketch generation using convolutional networks;analyzing pixel spread correlation with lenticular lens efficiency on multi user vr displays;anatomy-focused volume line integral convolution for brain white matter visualization;corotated meshless implicit dynamics for deformable bodies;and performance evaluation and comparison of service-based image processing based on software rendering.

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SAda-Net: A Self-supervised Adaptive Stereo Estimation CNN For Remote Sensing Image Data 27th

SAda-Net: A Self-supervised Adaptive Stereo Estimation CNN F...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Hirner, Dominik Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Cologne Germany

ISBN: (纸本)9783031781919

Stereo estimation has made many advancements in recent years with the introduction of deep-learning. However the traditional supervised approach to deep-learning requires the creation of accurate and plentiful ground-truth data, which is expensive to create and not available in many situations. this is especially true for remote sensing applications, where there is an excess of available data without proper ground truth. To tackle this problem, we propose a self-supervised CNN with self-improving adaptive abilities. In the first iteration, the created disparity map is inaccurate and noisy. Leveraging the left-right consistency check, we get a sparse but more accurate disparity map which is used as an initial pseudo ground-truth. this pseudo ground-truth is then adapted and updated after every epoch in the training step of the network. We use the sum of inconsistent points in order to track the network convergence. the code for our method will be made available after acceptance at https://***/thedodo/SAda-Net. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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computer vision, Imaging and computer graphics theory and Applications: 11th international Joint conference, VISIGRAPP 2016, Rome, Italy, February 27-29, 2016

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2017年

作者： Jos Braz Nadia Magnenat-thalmann Paul Richard Lars Linsen Alexandru C Telea Sebastiano Battiato Francisco Imai

ISBN: (纸本)9783319648699

this book constitutes thoroughly revised and selected papers from the 11th international Joint conference on computer vision, Imaging and computer graphics theory and Applications, VISIGRAPP 2016, held in Rome, Italy, in February 2016. VISIGRAPP comprises GRAPP, international conference on computer graphics theory and Applications; IVAPP, international conference on Information Visualization theory and Applications; and VISAPP, international conference on computer vision theory and Applications. the 28 thoroughly revised and extended papers presented in this volume were carefully reviewed and selected from 338 submissions. the book also contains one invited talk in full-paper length. the regular papers were organized in topical sections named: computer graphics theory and applications; information visualization theory and applications; and computer vision theory and applications.

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LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation 1st

LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar ...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th international conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： thaler, Franz Štern, Darko Plank, Gernot Urschler, Martin Gottfried Schatz Research Center: Medical Physics and Biophysics Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria AVL List GmbH Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria

ISBN: (纸本)9783031870088

Atrial fibrillation (AF) represents the most prevalent type of cardiac arrhythmia for which treatment may require patients to undergo ablation therapy. In this surgery cardiac tissues are locally scarred on purpose to prevent electrical signals from causing arrhythmia. Patient-specific cardiac digital twin models show great potential for personalized ablation therapy, however, they demand accurate semantic segmentation of healthy and scarred tissue typically obtained from late gadolinium enhanced (LGE) magnetic resonance (MR) scans. In this work we propose the Left Atrial Cascading Refinement CNN (LA-CaRe-CNN), which aims to accurately segment the left atrium as well as left atrial scar tissue from LGE MR scans. LA-CaRe-CNN is a 2-stage CNN cascade that is trained end-to-end in 3D, where Stage 1 generates a prediction for the left atrium, which is then refined in Stage 2 in conjunction with the original image information to obtain a prediction for the left atrial scar tissue. To account for domain shift towards domains unknown during training, we employ strong intensity and spatial augmentation to increase the diversity of the training dataset. Our proposed method based on a 5-fold ensemble achieves great segmentation results, namely, 89.21% DSC and 1.6969 mm ASSD for the left atrium, as well as 64.59% DSC and 91.80% G-DSC for the more challenging left atrial scar tissue. thus, segmentations obtained through LA-CaRe-CNN show great potential for the generation of patient-specific cardiac digital twin models and downstream tasks like personalized targeted ablation therapy to treat AF. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ablation

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Augmentation-Based Domain Generalization and Joint Training from Multiple Source Domains for Whole Heart Segmentation 1st

Augmentation-Based Domain Generalization and Joint Training...

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ISBN: (纸本)9783031870088

As the leading cause of death worldwide, cardiovascular diseases motivate the development of more sophisticated methods to analyze the heart and its substructures from medical images like Computed Tomography (CT) and Magnetic Resonance (MR). Semantic segmentations of important cardiac structures that represent the whole heart are useful to assess patient-specific cardiac morphology and pathology. Furthermore, accurate semantic segmentations can be used to generate cardiac digital twin models which allows e.g. electrophysiological simulation and personalized therapy planning. Even though deep learning-based methods for medical image segmentation achieved great advancements over the last decade, retaining good performance under domain shift - i.e. when training and test data are sampled from different data distributions - remains challenging. In order to perform well on domains known at training-time, we employ a (1) balanced joint training approach that utilizes CT and MR data in equal amounts from different source domains. Further, aiming to alleviate domain shift towards domains only encountered at test-time, we rely on (2) strong intensity and spatial augmentation techniques to greatly diversify the available training data. Our proposed whole heart segmentation method, a 5-fold ensemble with our contributions, achieves the best performance for MR data overall and a performance similar to the best performance for CT data when compared to a model trained solely on CT. With 93.33% DSC and 0.8388 mm ASSD for CT and 89.30% DSC and 1.2411 mm ASSD for MR data, our method demonstrates great potential to efficiently obtain accurate semantic segmentations from which patient-specific cardiac twin models can be generated. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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3D-EffiViTCaps: 3D Efficient vision Transformer with Capsule for Medical Image Segmentation 27th

3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsul...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Gan, Dongwei Chang, Ming Chen, Juan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783031781858

Medical image segmentation (MIS) aims to finely segment various organs. It requires grasping global information from both parts and the entire image for better segmenting, and clinically there are often certain requirements for segmentation efficiency. Convolutional neural networks (CNNs) have made considerable achievements in MIS. However, they are difficult to fully collect global context information and their pooling layer may cause information loss. Capsule networks, which combine the benefits of CNNs while taking into account additional information such as relative location that CNNs do not, have lately demonstrated some advantages in MIS. vision Transformer (ViT) employs transformers in visual tasks. Transformer based on attention mechanism has excellent global inductive modeling capabilities and is expected to capture long-range information. Moreover, there have been resent studies on making ViT more lightweight to minimize model complexity and increase efficiency. In this paper, we propose a U-shaped 3D encoder-decoder network named 3D-EffiViTCaps, which combines 3D capsule blocks with 3D EfficientViT blocks for MIS. Our encoder uses capsule blocks and EfficientViT blocks to jointly capture local and global semantic information more effectively and efficiently with less information loss, while the decoder employs CNN blocks and EfficientViT blocks to catch finer details for segmentation. We conduct experiments on various datasets, including iSeg-2017, Hippocampus and Cardiac to verify the performance and efficiency of 3D-EffiViTCaps, which performs better than previous 3D CNN-based, 3D Capsule-based and 3D Transformer-based models. We further implement a series of ablation experiments on the main blocks. Our code is available at: https://***/HidNeuron/3D-EffiViTCaps. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decoding

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Color Imaging in Medical Video Systems: Problems and Features

Color Imaging in Medical Video Systems: Problems and Feature...

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Digital Signal Processing and its Applications (DSPA), international conference on

作者： Natalia A. Obukhova Alexandr A. Motyko Alexandr A. Pozdeev Konstantin A. Smirnov SPbSU ETU “LETI” St. Petersburg Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

this paper explores the issue of accurate colour reproduction in medical imaging. It considers key problems and lists standards related to the process of forming and displaying medical images. Particular attention is paid to the fact that the existing standards do not take into account the individual characteristics of colour perception in different doctors. In the conditions of using modern displays with wide colour coverage this can lead to errors in colour rendering for a particular doctor, which, in turn, reduces the accuracy of disease diagnostics. the application of optimal LUT (Look-Up Table) with variable resolution to recalculate the colour space of the display is proposed, which allows to personalise the image for individual features of the doctor's vision and improve the accuracy of diagnosis. Experimental results demonstrating the advantages of the proposed approach over classical colour transform methods are presented. the results confirm that the use of variable resolution LUT can reduce colour rendering errors and improve the quality of medical diagnostics.

关键词： Image resolution Accuracy three-dimensional displays Image color analysis Transforms Observers Rendering (computer graphics) Table lookup Standards Signal resolution

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Aspects of computer graphics' learning within the course named «informatics» for non-core specialties and directions 27

Aspects of computer graphics' learning within the course nam...

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27th international conference on computer graphics and vision, graphicon 2017

作者： Vasiluk, N.N. PSNIU Perm Russia

ISBN: (纸本)9785794429633

In this article, the aspects of the methodical approach to teaching computer graphics within the course named «Informatics» on non-core specialties and directions in relation to this subject are described. the aim and objectives of the training are formulated within the framework of this topic, the selection of the content of the learning is described, primarily in terms of software selection. themes of laboratory works and approximate tasks are given. © graphicon 2017 - computer graphics and *** right reserved.

关键词： computer graphics

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