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检索条件"任意字段=International Conference on Recent Advances in 3-D Digital Imaging and Modeling"

共 333 条记录，以下是1-10 订阅

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Proceedings of the 1997 1st international conference on recent advances in 3-d digital imaging and modeling

Proceedings of the 1997 1st International Conference on Rece...

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Proceedings of the 1997 1st international conference on recent advances in 3-d digital imaging and modeling

The proceedings contains 45 papers from the international conference on recent advances in 3-d digital imaging and modeling. Topics discussed include: range image sensors;laser rangefinders;view planning;view registration;geometric processing;object modeling;human shape modeling;three dimensional facial caricaturing;three dimensional models database management;portable three-dimensional digitizers;and automatic computer aided design modeling.

关键词： imaging systems

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Proceedings. international conference on recent advances in 3-d digital imaging and modeling (Cat. No.97TB100134)

Proceedings. International Conference on Recent Advances in ...

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international conference on 3d imaging, modeling, Processing, Visualization and Transmission (3dIMPVT)

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Hierarchical Spatio-Temporal State-Space modeling for fMRI Analysis 29th

Hierarchical Spatio-Temporal State-Space Modeling for fMRI ...

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29th international conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Wei, Yuxiang Abrol, Anees Calhoun, Vince d. Atlanta United States

ISBN: (纸本)9783031902512

recent advances in deep learning structured state space models, especially the Mamba architecture, have demonstrated remarkable performance improvements while maintaining linear complexity. In this study, we introduce functional spatiotemporal Mamba (FST-Mamba), a Mamba-based model designed for discovering neurological biomarkers using functional magnetic resonance imaging (fMRI). We focus on dynamic functional network connectivity (dFNC) derived from fMRI and propose a hierarchical spatiotemporal Mamba-based network that processes spatial and temporal information separately using Mamba-based encoders. Leveraging the topological uniqueness of the FNC matrix, we introduce a component-wise varied-scale aggregation (CVA) mechanism to aggregate connectivity across individual components within brain networks, enabling the model to capture component-level and network-level information. Additionally, we propose symmetric rotary position encoding (SymRope) to encode the relative positions of each functional connection while considering the symmetric nature of the FNC matrix. Experimental results demonstrate significant improvements in the proposed FST-Mamba model on various brain-based classification and regression tasks. We further show brain connectivities and dynamics that are crucial for the prediction. Our work reveals the substantial potential of attention-free sequence modeling in brain discovery. The codes are publicly available here: https://***/yuxiangwei0808/FunctionalMamba/tree/main. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Brain mapping

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advances in Image Analysis: Leveraging Computer Vision for Automated Recognition and Interpretation

Advances in Image Analysis: Leveraging Computer Vision for A...

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Artificial Intelligence and data Engineering (AIdE), international conference on

作者： N S Kalyan Chakravarthy SK. Sheema Rajasekar Manda Jafar Ali Ibrahim S M. Senthil Department of CSE QIS College of Engineering and Technology Ongole AP India Department of ECE QIS College of Engineering and Technology Ongole AP India Institute of Computer Science and Digital Innovation UCSI University Taman Connaught 56000 Kuala Lumpur Wilayah Persekutuan Kuala Lumpur Malaysia

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Computer vision has revolutionized image analysis by enabling automated recognition and interpretation of complex visual data. This study delves into recent advancements in deep learning, focusing on convolutional neural networks (CNNs), attention mechanisms, and comparative evaluations with newer architectures such as Vision Transformers and Efficientdet. Using datasets such as ImageNet, COCO, and NIH Chest X-ray, our model demonstrated a 20% improvement in classification accuracy compared to YOLOv5 and a 12% improvement over Efficientdet on medical imaging tasks. Strategies to address challenges like occlusion, lighting variability, and false positives are discussed. Our findings highlight significant improvements in classification accuracy, object detection precision, and computational efficiency, paving the way for scalable and real-time applications in healthcare, autonomous systems, and industrial automation.

关键词： YOLO Computer vision Accuracy Attention mechanisms Image recognition Computational modeling Transformers Real-time systems Computational efficiency Biomedical imaging

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Classification of 3d dynamic Contrast Enhanced Breast Tumour MRI Images Using CNN

Classification of 3D Dynamic Contrast Enhanced Breast Tumour...

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international conference on Signal Processing and Communication (ICSC)

作者： Anjana divakaran J. Glory. Precious S.P. Angeline Kirubha Department of Biomedical Engineering SRM Institute of Science and Technology Kattankulathur Chennai Department of Biomedical Engineering Raja Lakshmi Engineering College Thandalam Chennai

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Breast cancer remains the most prevalent malignant tumor in women worldwide and a significant contributor to female mortality. dynamic contrast-enhanced MRI (dCE-MRI) is an emerging technique for the diagnosis of breast disease, and it is highly efficient in the recognition, observation, and diagnosis of tumors. Additionally, there is hope that more advanced applications for early breast cancer diagnosis may be developed by recent advances in image processing and deep learning. In our study, a 3d CNN deep learning tool was designed to classify breast tumors into stages of malignancy in contrast to prior works that generally classify images as benign or malignant. Firstly, the 3d dCE-MRI images are preprocessed and then split into training and validation sets. The train set was augmented, and combined data was fed into 3d CNN, which provided 81% training accuracy and 71% validation accuracy. Further work, a prediction application program interface was created using our pretrained model in which the stages for each of the 3d images loaded were predicted. The hypothesized 3d CNN model has shown good accuracy in classifying the four distinct stages of breast tumors, namely stage 1, stage 2, stage 3, and stage 4. This can aid radiologists in making more accurate predictions and in the early diagnosis of breast tumors.

关键词： deep learning Training Solid modeling Three-dimensional displays Breast tumors Accuracy Magnetic resonance imaging Breast cancer Convolutional neural networks Load modeling

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

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

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advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

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

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

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.

关键词： Solid modeling Accuracy Three-dimensional displays Minimally invasive surgery Costs Navigation Surgery Machine learning Real-time systems Augmented reality

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A Real-Time Simulator for High-Speed Structured-Light Cameras in Robotic Applications with Optimal Positioning Evaluation

A Real-Time Simulator for High-Speed Structured-Light Camera...

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international conference on Autonomous Robots and Agents, ICARA

作者： Gilles Rougeron Jérémie Le Garrec Claude Andriot Université Paris-Saclay CEA List Palaiseau France

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

With recent advances in industrial robotics, autonomous robotic arms need to deal with complex environments using sensors such as depth cameras. Structured-light cameras are often employed for this purpose as they provide high precision measurements and increasing speed acquisition. Furthermore, as digital twins are becoming more common for training and guiding real robots, there is a growing need for high-performance sensor simulations. This paper thus introduces a fast, GPU-implemented, and realistic simulation tool for structured-light depth cameras. It can compute 3d point clouds with noisy coordinates in real-time for a set of cameras in complex, dynamic scenes simulated by a physics engine. It can also interactively evaluate coverage and overlap of simulated point clouds on objects of interest. Finally, by maximizing their values, as a pre-process or during simulation for motionless scenes, it allows optimal cameras positioning. To demonstrate the capabilities of our simulation tool, we present two use cases. The first showcases a real-time simulation of a high-speed structured-light camera operating at 20 frames per second, mounted on a robotic arm that dismantles an electrical battery. The second illustrates the optimal positioning of a set of fixed structured-light cameras in a cluttered environment, where a robotic arm performs the cutting of a glove box. To our knowledge, our tool is the first to offer such real-time simulations and rapid structured-light camera positioning in complex robotic environments.

关键词： Point cloud compression Service robots Robot kinematics Computational modeling Robot vision systems Cameras Manipulators Real-time systems Sensors digital twins

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Physics-based models for image analysis/synthesis and geometric design

Physics-based models for image analysis/synthesis and geomet...

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international conference on recent advances in 3-d digital imaging and modeling

作者： Terzopoulos, d Univ of Toronto Toronto Canada

ISBN: (纸本)0818679441

This invited paper reviews recently developed physics-based surface modeling techniques for geometric design, medical image analysis, and human facial modeling. I briefly motivate the problems of interest in each application area, describe the models that we have developed to address them, present sample results, and provide references to technical papers containing the full details.

关键词： data structures

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3d modeling of human hand with motion constraints

3D modeling of human hand with motion constraints

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international conference on recent advances in 3-d digital imaging and modeling

作者： Yasumuro, Y Chen, Q Chihara, K Nara Inst of Science and Technology Nara Japan

ISBN: (纸本)0818679441

This paper describes a new approach for modeling of the human hand by considering the dynamics and the natural constraints of the motion. and the shape of hands. The hand model consists of a dynamic model and a surface model. The dynamic model is used to generate the posture of the hand. We show that natural hand posture can, be generated even only a few hand parameters are available. The surface model is used to generate the hand shape based on the posture obtained from the dynamic model. The surface model is built based on the digitized 3d shape of a real hand.

关键词： Object recognition

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3d imager for dimensional gauging of industrial workpieces: State of the art of the development of a robust and versatile system

3D imager for dimensional gauging of industrial workpieces: ...

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international conference on recent advances in 3-d digital imaging and modeling

作者： Sansoni, G Lazzari, S Peli, S docchio, F Univ of Brescia Brescia Italy

ISBN: (纸本)0818679441

In our laboratory, the performance of a 3d imaging system, based on structured light projection, has been improved by means of the development of three procedures, which are based on (i) the Gray Code projection Method (GCM), (ii) the Phase Shift Method (PSM), and (iii) the combination of GCM and PSM. In this paper, all the procedures are described and the results of the measurement performed on target objects are presented. The analysis of the systematic height error due to the crossed axes optical geometry of the system is performed, and the procedure for its compensation is presented.

关键词： imaging systems

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