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

共 54122 条记录，以下是4721-4730 订阅

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Modelling and Dynamic Simulation of Palletizing Robot System Based on Multibody

Modelling and Dynamic Simulation of Palletizing Robot System...

引用

ieee information Technology, Networking, Electronic and Automation Control conference

作者： Zhang Zhuo Zhang Cheng Deng Zhenjie Feng Haijie College of Robotics Guangdong Polytechnic of Science and Technology School of Electronic and Information Engineering Zhuhai College of Science and Technology

In order to solve the problems of lack of port for motion planning and motion control, difficulty of changing kinetic parameters depending on the robot structure, and lack of visualization ability of kinetic simulation in the conventional modeling and simulation of palletizing robots, a hybrid 4-axis multi-joint palletizing robot system modeling method is designed and simulated. The kinematic analysis of the palletizing robot is carried out, the MBS model of the robot is created using Simscape, the motion trajectory is planned, and the dynamics simulation is carried out to verify the correctness of the system model. The experimental results show that the system model has better adaptability and functionality than conventional modeling and simulation methods, and can provide a complete R&D platform for the design of similar robots and the study of control algorithms, which is of good scientific significance.

关键词： Adaptation models Analytical models visualization Heuristic algorithms Dynamics Planning Kinetic theory

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Augmented and Mixed Reality based Decision Support Tool for the Integrated Resource Plan 47

Augmented and Mixed Reality based Decision Support Tool for ...

引用

47th Annual conference of the ieee-Industrial-Electronics-Society (IECON)

作者： Govender, Devashen Moodley, Jayandren Balmahoon, Reevana CSIR Pretoria South Africa

ISBN: (纸本)9781665435543

In today's era, enormous amounts of data are generated and the means to visualize this data is becoming a challenge. Data visualization is an important support tool that allows one to make informed decisions. This paper attempts to provide a possible solution to this major challenge. The solution presented is a data analytic tool capable of visualizing complex data by leveraging the fourth industrial revolution technologies, augmented and mixed reality. Using the disruptive nature of these technologies, it could provide an application that is more intuitive and immersive to the end-user. Moreover, we focus the solution in the energy domain by exploring methods in enhancing energy data visualization to support decision-making of South Africa's Integrated Resource Plan (IRP).

关键词： augmented reality data visualization data analysis

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Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning

Good, Better, Best: Textual Distractors Generation for Multi...

引用

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

作者： Lu, Jiaying Ye, Xin Ren, Yi Yang, Yezhou Emory Univ Dept Comp Sci Atlanta GA 30322 USA Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85287 USA Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA Arizona State Univ Tempe AZ 85287 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Multiple-choice VQA has drawn increasing attention from researchers and end-users recently. As the demand for automatically constructing large-scale multiple-choice VQA data grows, we introduce a novel task called textual Distractors Generation for VQA (DG-VQA) focusing on generating challenging yet meaningful distractors given the context image, question, and correct answer. The DG-VQA task aims at generating distractors without ground-truth training samples since such resources are rarely available. To tackle the DG-VQA unsupervisedly, we propose GOBBET, a reinforcement learning(RL) based framework that utilizes pre-trained VQA models as an alternative knowledge base to guide the distractor generation process. In GOBBET, a pre-trained VQA model serves as the environment in RL setting to provide feedback for the input multi-modal query, while a neural distractor generator serves as the agent to take actions accordingly. We propose to use existing VQA models' performance degradation as indicators of the quality of generated distractors. On the other hand, we show the utility of generated distractors through data augmentation experiments, since robustness is more and more important when AI models apply to unpredictable open-domain scenarios or security-sensitive applications. We further conduct a manual case study on the factors why distractors generated by GOBBET can fool existing models.

关键词： Training visualization Computational modeling Knowledge based systems Training data Reinforcement learning Data models

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WANDERLUST: 3D IMPRESSIONISM IN HUMAN JOURNEYS

WANDERLUST: 3D IMPRESSIONISM IN HUMAN JOURNEYS

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ieee conference on VIS Arts Program (VISAP)

作者： Du, Guangyu Duarte, Fabio Dong, Lei Ratti, Carlo MIT Senseable City Lab Cambridge MA 02139 USA Peking Univ Beijing Peoples R China

ISBN: (纸本)9781665440219

The movements of individuals are fundamental to building and maintaining social connections. This pictorial presents Wanderlust, an experimental three-dimensional (3D) data visualization on the universal visitation pattern revealed from large-scale mobile phone tracking data. It explores ways of visualizing recurrent flows and the attractive places they implied. Inspired by the 19th-century art movement Impressionism, we develop a multi-layered effect, an `impression', of mountains emerging from consolidated flows, to capture the essence of human journeys and urban spatial structure.

关键词： 3D visualization impressionism flow map complex systems data art

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Autonomous Aerial Vehicle Vision and Sensor Guided Landing

Autonomous Aerial Vehicle Vision and Sensor Guided Landing

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ieee International conference on Electro information Technology (EIT)

作者： Bitencourt, Gabriel Brown, Elijah Bleimnling, Cedric Lai, Gilbert Molki, Arman Kaya, Tolga Sacred Heart Univ Sch Comp Sci & Engn Jack Welch Coll Business Fairfield CT 06825 USA Quanser Res Software Quanser ON Canada Quanser Acad Applicat Markham ON Canada

ISBN: (纸本)9781665418461

The use of autonomous landing of aerial vehicles is increasing in demand. Applications of this ability can range from simple drone delivery to unmanned military missions. To be able to land at a spot identified by local information, such as a visual marker, creates an efficient and versatile solution. This allows for a more user/consumer friendly device overall. To achieve this goal the use of computer vision and an array of ranging sensors will be explored. In our approach we utilized an April Tag as our location identifier and point of reference. MATLAB/Simulink interface was used to develop the platform environment.

关键词： visualization Computer vision Military computing conferences Distance measurement Sensors information technology

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Learnable Irrelevant Modality Dropout for Multimodal Action Recognition on Modality-Specific Annotated Videos

Learnable Irrelevant Modality Dropout for Multimodal Action ...

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

作者： Alfasly, Saghir Lu, Jian Xu, Chen Zou, Yuru Shenzhen Univ Shenzhen Key Lab Adv Machine Learning & Applicat Shenzhen Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

With the assumption that a video dataset is multimodality annotated in which auditory and visual modalities both are labeled or class-relevant, current multimodal methods apply modality fusion or cross-modality attention. However, effectively leveraging the audio modality in vision-specific annotated videos for action recognition is of particular challenge. To tackle this challenge, we propose a novel audio-visual framework that effectively leverages the audio modality in any solely vision-specific annotated dataset. We adopt the language models (e.g., BERT) to build a semantic audio-video label dictionary (SAVLD) that maps each video label to its most K-relevant audio labels in which SAVLD serves as a bridge between audio and video datasets. Then, SAVLD along with a pretrained audio multi-label model are used to estimate the audio-visual modality relevance during the training phase. Accordingly, a novel learnable irrelevant modality dropout (IMD) is proposed to completely drop out the irrelevant audio modality and fuse only the relevant modalities. Moreover, we present a new two-stream video Transformer for efficiently modeling the visual modalities. Results on several vision-specific annotated datasets including Kinetics400 and UCF-101 validated our frame-work as it outperforms most relevant action recognition methods.

关键词： Training Bridges visualization Fuses Computational modeling Semantics Transfer learning

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Ranking-Based Siamese Visual Tracking

Ranking-Based Siamese Visual Tracking

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

作者： Tang, Feng Ling, Qiang Univ Sci & Technol China Dept Automat Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current Siamese-based trackers mainly formulate the visual tracking into two independent subtasks, including classification and localization. They learn the classification subnetwork by processing each sample separately and neglect the relationship among positive and negative samples. Moreover, such tracking paradigm takes only the classification confidence of proposals for the final prediction, which may yield the misalignment between classification and localization. To resolve these issues, this paper proposes a ranking-based optimization algorithm to explore the relationship among different proposals. To this end, we introduce two ranking losses, including the classification one and the IoU-guided one, as optimization constraints. The classification ranking loss can ensure that positive samples rank higher than hard negative ones, i.e., distractors, so that the trackers can select the foreground samples successfully without being fooled by the distractors. The IoU-guided ranking loss aims to align classification confidence scores with the Intersection over Union(IoU) of the corresponding localization prediction for positive samples, enabling the well-localized prediction to be represented by high classification confidence. Specifically, the proposed two ranking losses are compatible with most Siamese trackers and incur no additional computation for inference. Extensive experiments on seven tracking benchmarks, including OTB100, UAV123, TC128, VOT2016, NFS30, GOT-10k and LaSOT, demonstrate the effectiveness of the proposed ranking-based optimization algorithm. The code and raw results are available at https: //***/sansanfree/RBO.

关键词： Location awareness visualization Computer vision Tracking Prediction algorithms Inference algorithms Classification algorithms

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LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane

LF-VIO: A Visual-Inertial-Odometry Framework for Large Field...

引用

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

作者： Wang, Ze Yang, Kailun Shi, Hao Li, Peng Gao, Fei Wang, Kaiwei Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou Peoples R China Karlsruhe Inst Technol Inst Anthropomat & Robot Karlsruhe Germany Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China Zhejiang Univ Huzhou Inst Hangzhou Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Visual-inertial-odometry has attracted extensive attention in the field of autonomous driving and robotics. The size of Field of View (FoV) plays an important role in Visual-Odometry (VO) and Visual-Inertial-Odometry (VIO), as a large FoV enables to perceive a wide range of surrounding scene elements and features. However, when the field of the camera reaches the negative half plane, one cannot simply use [u, v, 1](T) to represent the image feature points anymore. To tackle this issue, we propose LF-VIO, a real-time VIO framework for cameras with extremely large FoV. We leverage a three-dimensional vector with unit length to represent feature points, and design a series of algorithms to overcome this challenge. To address the scarcity of panoramic visual odometry datasets with ground-truth location and pose, we present the PALVIO dataset, collected with a Panoramic Annular Lens (PAL) system with an entire FoV of 360 degrees x (40 degrees similar to 120 degrees) and an IMU sensor. With a comprehensive variety of experiments, the proposed LF-VIO is verified on both the established PALVIO benchmark and a public fisheye camera dataset with a FoV of 360 degrees x (0 degrees similar to 93.5 degrees). LF-VIO outperforms state-of-the-art visual-inertial-odometry methods. Our dataset and code are made publicly available at https://***/flysoaryun/LF-VIO

关键词： visualization Simultaneous localization and mapping Robot vision systems Pose estimation Cameras Real-time systems Robustness

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Finding Fallen Objects Via Asynchronous Audio-Visual Integration

Finding Fallen Objects Via Asynchronous Audio-Visual Integra...

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

作者： Gan, Chuang Gu, Yi Zhou, Siyuan Schwartz, Jeremy Alter, Seth Traer, James Gutfreund, Dan Tenenbaum, Joshua B. McDermott, Josh H. Torralba, Antonio MIT 77 Massachusetts Ave Cambridge MA 02139 USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The way an object looks and sounds provide complementary reflections of its physical properties. In many settings cues from vision and audition arrive asynchronously but must be integrated, as when we hear an object dropped on the floor and then must find it. In this paper, we introduce a setting in which to study multi-modal object localization in 3D virtual environments. An object is dropped somewhere in a room. An embodied robot agent, equipped with camera and microphone, must determine what object has been dropped - and where - by combining audio and visual signals with knowledge of the underlying physics. To study this problem, we have generated a large-scale dataset - the Fallen Objects dataset - that includes 8000 instances of 30 physical object categories in 64 rooms. The dataset uses the ThreeDWorld Platform that can simulate physics-based impact sounds and complex physical interactions between objects in a photorealistic setting. As a first step toward addressing this challenge, we develop a set of embodied agent baselines, based on imitation learning, reinforcement learning, and modular planning, and perform an in-depth analysis of the challenge of this new task. This dataset is publicly available(1).

关键词： Location awareness visualization Three-dimensional displays Robot vision systems Virtual environments Reinforcement learning Reflection

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Real Image Super-Resolution using GAN through modeling of LR and HR process 18

Real Image Super-Resolution using GAN through modeling of LR...

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18th ieee International conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Umer, Rao Muhammad Micheloni, Christian Helmholtz Munich Inst AI Hlth AIH Munich Germany Univ Udine Dept Math & Comp Sci Udine Italy

ISBN: (纸本)9781665463829

The current existing deep image super-resolution methods usually assume that a Low Resolution (LR) image is bicubicly downscaled of a High Resolution (HR) image. However, such an ideal bicubic downsampling process is different from the real LR degradations, which usually come from complicated combinations of different degradation processes, such as camera blur, sensor noise, sharpening artifacts, JPEG compression, and further image editing, and several times image transmission over the internet and unpredictable noises. It leads to the highly ill-posed nature of the inverse upscaling problem. To address these issues, we propose a GAN-based SR approach with learnable adaptive sinusoidal nonlinearities incorporated in LR and SR models by directly learn degradation distributions and then synthesize paired LR/HR training data to train the generalized SR model to real image degradations. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments.

关键词： Degradation Adaptation models visualization Surveillance Image communication Superresolution Training data

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