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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

共 54227 条记录，以下是4011-4020 订阅

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A Metric for Finding Robust Start Positions for Medical Steerable Needle Automation

A Metric for Finding Robust Start Positions for Medical Stee...

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Hoelscher, Janine Fried, Inbar Fu, Mengyu Patwardhan, Mihir Christman, Max Akulian, Jason Webster, Robert J., III Alterovitz, Ron Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA Univ N Carolina Div Pulm Dis & Crit Care Med Chapel Hill NC 27599 USA Vanderbilt Univ Dept Mech Engn Nashville TN 37235 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Steerable needles are medical devices with the ability to follow curvilinear paths to reach targets while circumventing obstacles. In the deployment process, a human operator typically places the steerable needle at its start position on a tissue surface and then hands off control to the automation that steers the needle to the target. Due to uncertainty in the placement of the needle by the human operator, choosing a start position that is robust to deviations is crucial since some start positions may make it impossible for the steerable needle to safely reach the target. We introduce a method to efficiently evaluate steerable needle motion plans such that they are safe to variation in the start position. This method can be applied to many steerable needle planners and requires that the needle's orientation angle at insertion can be robotically controlled. Specifically, we introduce a method that builds a funnel around a given plan to determine a safe insertion surface corresponding to insertion points from which it is guaranteed that a collisionfree motion plan to the goal can be computed. We use this technique to evaluate multiple feasible plans and select the one that maximizes the size of the safe insertion surface. We evaluate our method through simulation in a lung biopsy scenario and show that the method is able to quickly find needle plans with a large safe insertion surface.

关键词： visualization Uncertainty Automation Medical devices Process control Needles Size measurement

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Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer

Not All Tokens Are Equal: Human-centric Visual Analysis via ...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zeng, Wang Jin, Sheng Liu, Wentao Qian, Chen Luo, Ping Ouyang, Wanli Wang, Xiaogang Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China SenseTime Res & Tetras AI Hangzhou Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Vision transformers have achieved great successes in many computer vision tasks. Most methods generate vision tokens by splitting an image into a regular and fixed grid and treating each cell as a token. However, not all regions are equally important in human-centric vision tasks, e.g., the human body needs a fine representation with many tokens, while the image background can be modeled by a few tokens. To address this problem, we propose a novel Vision Transformer, called Token Clustering Transformer (TCFormer), which merges tokens by progressive clustering, where the tokens can be merged from different locations with flexible shapes and sizes. The tokens in TCFormer can not only focus on important areas but also adjust the token shapes to fit the semantic concept and adopt a fine resolution for regions containing critical details, which is beneficial to capturing detailed information. Extensive experiments show that TCFormer consistently outperforms its counterparts on different challenging human-centric tasks and datasets, including whole-body pose estimation on COCO-WholeBody and 3D human mesh reconstruction on 3DPW. Code is available at https://***/zengwang430521/***.

关键词： Computer vision visualization Three-dimensional displays Shape Pose estimation Semantics Computer architecture

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Improving GAN-based Domain Adaptation for Object Detection 25

Improving GAN-based Domain Adaptation for Object Detection

引用

ieee 25th International conference on Intelligent Transportation Systems (ITSC)

作者： Menke, Maximilian Wenzel, Thomas Schwung, Andreas Robert Bosch Car Multimedia GmbH Hildesheim Germany Univ Appl Sci Soest Germany

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Unlabeled or insufficiently labeled data is a common challenge in autonomous driving applications. Perception tasks like object detection in particular require annotated data, which is expensive to acquire, due to mostly manual labeling processes. One approach to saving label cost is using synthetic data and unsupervised domain adaptation to bridge the visual appearance gap towards the real application data. Knowing the placement of objects in rendered scenes, labels can be extracted automatically. However, lack of realism in rendered scenes leaves a domain gap between real and synthetic data. We transfer image-to-image adversarial domain adaptation methods from semantic segmentation to object detection, leveraging synthetic data for the task of vehicle detection in real images. This improved GAN-based image generation is combined with a standard object detector. This is in contrast to previous works, which also applied domain adaptation methods to the detector. With our adaptations we achieve state of the art results in domain adaption using generative adversarial networks on GTA to Cityscapes synthetic to real unsupervised domain transfer for vehicle detection.

关键词： Training visualization Vehicle detection Semantics Object detection Detectors Manuals

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3D visualization of Indoor Thermal Environment with UE5

3D Visualization of Indoor Thermal Environment with UE5

引用

ieee International conference on Orange Technologies (ICOT)

作者： Xu Pushi Computer Science and Information Engineering Tamkang University Taichuang Taiwan China

ISBN: (数字)9798331523602

ISBN: (纸本)9798331523619

This study focuses on the development of an advanced indoor thermal environment visualization system utilizing UE5, leveraging various blueprint features such as character blueprints and widget blueprints to implement interactive functionalities and dynamic simulations. The system effectively simulates indoor and outdoor heat transfer processes, allowing users to visualize and analyze key parameters of building facades with high accuracy. By providing intuitive thermal environment data, it aids in informed decision-making for architectural designs. Future work will focus on enhancing thermal calculation accuracy, streamlining data import workflows, and improving interactivity to create a more robust and user-friendly system for advanced thermal simulations.

关键词： Accuracy Three-dimensional displays Decision making Data visualization Rendering (computer graphics) Steady-state Windows Thermal analysis Optimization Heat transfer

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Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model

Amodal Segmentation through Out-of-Task and Out-of-Distribut...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Sun, Yihong Kortylewski, Adam Yuille, Alan Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms. The aim is to segment those object boundaries which are occluded and hence invisible. This task is particularly challenging for deep neural networks because data is difficult to obtain and annotate. Therefore, we formulate amodal segmentation as an out-of-task and out-of-distribution generalization problem. Specifically, we replace the fully connected classifier in neural networks with a Bayesian generative model of the neural network features. The model is trained from non-occluded images using bounding box annotations and class labels only, but is applied to generalize out-of-task to object segmentation and to generalize out-of-distribution to segment occluded objects. We demonstrate how such Bayesian models can naturally generalize beyond the training task labels when they learn a prior that models the object's background context and shape. Moreover, by leveraging an outlier process, Bayesian models can further generalize out-of-distribution to segment partially occluded objects and to predict their amodal object boundaries. Our algorithm outperforms alternative methods that use the same supervision by a large margin, and even outperforms methods where annotated amodal segmentations are used during training, when the amount of occlusion is large.

关键词： Training Image segmentation Computer vision visualization Shape Neural networks Predictive models

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Visual Analysis of Travel Behavior for Urban Rail Transit

Visual Analysis of Travel Behavior for Urban Rail Transit

引用

ieee Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Haiming Liu Kexin Li Zhi Cai School of Software Engineering Beijing Jiaotong University Beijing China School of Computer Science Beijing University of Technology Beijing China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Analyzing travel behavior of inhabitant can provide guidance and suggestions for urban traffic management and planning, land use planning, etc. In this paper, we designed a system to analyze the travel behavior of inhabitant using the smart card data of urban rail transit from two aspects, spatial visualization analysis and temporal visualization analysis. In spatial visualization analysis, we first conducted spatial and temporal aggregation for the whole city's smart card data. Then, we designed a spatial chord glyph to display the aggregated data. In temporal visualization analysis, we first proposed an algorithm to determine the degree and direction of cross-regional flow. Then, we study the degree of stability based on the algorithm. We conducted experiments based on urban rail transit in Beijing, the results show that our system can provide appropriate advice and support for urban traffic management and planning as well as land use planning.

关键词： Rails Smart cards visualization Urban areas Land use planning Data visualization Smart systems Stability analysis Spatial databases Planning

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Self-Recalibrating Micromanipulator System for Resilient Robotic Vision-Based Control 18

Self-Recalibrating Micromanipulator System for Resilient Rob...

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ieee 18th International conference on Automation Science and Engineering (ieee CASE)

作者： Wang, Tiexin Li, Haoyu Pu, Tanhong Ding, Jingjing Du, Shoukang Chau, Zhong Hoo Tan, U-Xuan Chew, Ting Gang Yang, Liangjing Zhejiang Univ ZJU UIUC Inst Int Campus Haining Zhejiang Peoples R China Zhejiang Univ Sch Med Zhejiang Univ Univ Edinburgh Inst Hangzhou 310058 Peoples R China Singapore Univ Technol & Design Engn Prod Dev Singapore Singapore

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

A self-recalibrating micromanipulator system is developed in this work to perform vision-based control under unpredictable scenes and poor imaging conditions. To ensure resilient vision-based control in such challenging conditions, we proposed a self-recalibrating mechanism that combines the estimates from both the robot manipulator and microscope vision. Our method is demonstrated for micropipette cell aspiration. In the initialization stage, the system automatically locates the tip and selects the templates for visual tracking. At the self-calibration phase, accurate tracking of the tip is achieved by the background subtraction template matching (BSTM) method in order to estimate the calibration matrix in complex environments. During the operation, the control error is detected according to the visual information and the self-recalibration is executed when the error exceeds the threshold value. Experimental analysis shows that the BSTM method for self-calibration achieves accurate tracking with an average error of less than 1 pixel (0.18 mu m). And the system has an average control error of 4.18 pixels ( 0.75 mu m) under complex calibration backgrounds, satisfying the accuracy requirements for cell aspiration. Moreover, the system achieved stable tracking in complex operating environments, including cell interaction, occlusion, tip leaving the focal plane or beyond the field-of-view. By designing a self-recalibrating micromanipulator equipped with resilient vision-based control system, we hope to robotize the procedures for the study of cell mechanics during micropipette aspiration and pave the path for the advancement of robotic micromanipulation in biomechanical research.

关键词： Biomechanics visualization Computer aided software engineering Automation Micromanipulators Microscopy Control systems

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CrossLocate: Cross-modal Large-scale Visual Geo-Localization in Natural Environments using Rendered Modalities 22

CrossLocate: Cross-modal Large-scale Visual Geo-Localization...

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22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Tomesek, Jan Cadik, Martin Brejcha, Jan Brno Univ Technol Fac Informat Technol CPhoto FIT Bozetechova 2 Brno 61200 Czech Republic

ISBN: (纸本)9781665409155

We propose a novel approach to visual geo-localization in natural environments. This is a challenging problem due to vast localization areas, the variable appearance of outdoor environments and the scarcity of available data. In order to make the research of new approaches possible, we first create two databases containing "synthetic" images of various modalities. These image modalities are rendered from a 3D terrain model and include semantic segmentations, silhouette maps and depth maps. By combining the rendered database views with existing datasets of photographs (used as "queries" to be localized), we create a unique benchmark for visual geo-localization in natural environments, which contains correspondences between query photographs and rendered database imagery. The distinct ability to match photographs to synthetically rendered databases defines our task as "cross-modal". On top of this benchmark, we provide thorough ablation studies analysing the localization potential of the database image modalities. We reveal the depth information as the best choice for outdoor localization. Finally, based on our observations, we carefully develop a fully-automatic method for large-scale cross-modal localization using image retrieval. We demonstrate its localization performance outdoors in the entire state of Switzerland. Our method reveals a large gap between operating within a single image domain (e.g. photographs) and working across domains (e.g. photographs matched to rendered images), as gained knowledge is not transferable between the two. Moreover, we show that modern localization methods fail when applied to such a cross-modal task and that our method achieves significantly better results than state-of-the-art approaches. The datasets, code and trained models are available on the project website: http://***/crosslocate/.

关键词： Location awareness visualization Solid modeling Image segmentation Three-dimensional displays Semantics Image retrieval

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Online Extrinsic Correction of Multi-Camera Systems by Low-Dimensional Parameterization of Physical Deformation

Online Extrinsic Correction of Multi-Camera Systems by Low-D...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Yang, Sangheon Lim, Jongwoo Hanyang Univ Dept Artificial Intelligence Seoul South Korea Hanyang Univ Dept Comp Sci Seoul South Korea MultiplEYE Co Ltd Seoul South Korea

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

In this paper, we propose the online extrinsic correction method that effectively optimizes the extrinsic parameters of multi-camera systems used in visual SLAM. In the typical visual SLAM systems that use multi-camera settings, the intrinsic and extrinsic parameters of the cameras are calculated through offline calibration, which is used as the fixed constraints in online execution. However, the camera rig can be physically deformed by shock or vibration, and the deviation from the offline calibration parameters can adversely affect the accuracy of triangulation and pose estimation. Therefore, it is crucial to maintain the accurate calibration of the camera rigs continuously throughout the execution. The previous online calibration methods optimize the extrinsic camera parameters in a full degree of freedom(DoF) by minimizing the reprojection error, but the limited visual information available online may bias the resulting camera poses. From the observation that the cameras are mounted on a physical body and the patterns that the body can be deformed is restricted and not completely free, we propose to model the pattern of physical rig deformation by external forces in advance, and then use the pre-trained low-dimensional deformation model to robustly and accurately estimate the changed camera poses in real-time. The proposed method consists of two steps. First, the physical model of the camera system is constructed in a simulator and the actual deformations by various external disturbances are recorded, and the deformation patterns are modeled by a PCA algorithm to build a low-dimensional model. In online execution, the camera poses are updated by minimizing the reprojection errors of visual features within the pre-trained low-dimensional parameterization, instead of optimizing all camera poses independently. Through the experiments in synthetic environments, the proposed online extrinsic correction method shows that it produces more accurate and robust camera pos

关键词： Deformable models Vibrations visualization Simultaneous localization and mapping Pose estimation Cameras Mathematical models

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A Parkinsonian Digital Biomarker Learned as an Anomaly Deep Generative Representation 44

A Parkinsonian Digital Biomarker Learned as an Anomaly Deep ...

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44th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Rangel, Edgar Martinez, Fabio Univ Ind Santander UIS Biomed Imaging Vis & Learning Lab BIVL2Ab Bucaramanga Colombia

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Parkinson's Disease (PD), the second most common neurodegenerative disorder, is associated with voluntary movement disorders caused by progressive dopamine deficiency. Gait motor alterations constitute a main tool to diagnose, characterize and personalize treatments. Nonetheless, such evaluation is biased by expert observations, reporting a false positive diagnosis up to 24%. Learning computational tools are recently emerged as potential alternatives to support diagnosis and to quantify kinematic patterns during locomotion. Nonetheless, such learning schemes required a large amount of balanced and stratified data examples, which may result unrealistic in clinical scenarios. This work introduces a self-supervised generative representation to discover gait-motion related patterns, under the pretext of video reconstruction and an anomaly detection framework. From the learned scheme, it is recovered a hidden embedding gait descriptor that constitutes a digital biomarker, allowing to discover PD differences regarding a control population. The proposed approach was validated with 11 PD patients (H&Y scale between 2.5 and 3.0) and 11 control subjects, and trained with only control population, achieving an AUC of 99.4% in the classification task.

关键词： visualization Parkinson's disease Biological system modeling Sociology Kinematics Statistics Task analysis

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