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检索条件"任意字段=Proceedings of the 3rd International Conference on Control and Computer Vision"

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Acquiring Accurate Quality Digital Entities For Digital Twins within Virtual Reality

Acquiring Accurate Quality Digital Entities For Digital Twin...

引用

Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Akram Taghavi-Burris Bridget Kurr Hawken Grubbs Tandy School of Computer Science University of Tulsa Tulsa USA

Creating digital entities (3D models) of digital twins for smart installations requires a number of considerations, especially when developing these to run on standalone virtual reality (VR) platforms. This paper provides an overview of the methods used for creating these digital entities for use with the Virtual Immersive, Remote Sensing, and Actuation (VIRSA) system. It explores conventional and cutting-edge techniques such as 3D scanning, laser-based surveys, photogrammetry, and procedural modeling. Additionally, the paper includes a thorough cost comparison between outsourcing and in-house model creation, considering factors such as initial investment, ongoing expenses, quality control, and scalability. Future possibilities in digital entity creation are discussed, envisioning advancements in this area. By equipping stakeholders with this knowledge, the research aims to optimize digital twin development, fostering innovation and enhancing the effectiveness of smart installations in various industries and applications.

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Path Planning and Algorithm Design of Automatic Pathfinding Technology in Virtual Reality Scene

Path Planning and Algorithm Design of Automatic Pathfinding ...

引用

Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Xiaocheng Zhou Lei Ding Jin Yue Xiaomei Ding Xiao Chen Shaowei Zhang Computer Engineering College Anhui Wenda University of Information Engineering Hefei Anhui China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

The purpose of Unity's automatic wayfinding technology is to enable characters or objects in a game or virtual reality scene to move autonomously and avoid obstacles, thereby enhancing the realism and interactivity of the game or scene. It mainly solves the problem of how to achieve accurate movement and obstacle avoidance in complex scenes, while ensuring operational efficiency and fluency. The solution to Unity's automatic pathfinding technology is primarily based on Navigation Mesh and path planning algorithms. This technology has been widely used in game development, virtual reality, robot control and other fields, and has obtained good results and feedback.

关键词： Legged locomotion Navigation Robot control Transportation Games Virtual reality Path planning Safety Collision avoidance Sports

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Advances in Deep Learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy

Advances in Deep Learning for Tuberculosis Detection in Ches...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Jayakumar K G Muthupandi Dhayanidhi R S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model.

关键词： Deep learning Accuracy Tuberculosis Transfer learning Medical services Feature extraction X-ray imaging Optimization Tuning Biomedical imaging

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Unsupervised Optical Flow Estimation Based on Multi-Scale Feature

Unsupervised Optical Flow Estimation Based on Multi-Scale Fe...

引用

international conference on Artificial Intelligence and computer Information Technology (AICIT)

作者： Chen Huang Qiong Li Ping Lv School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China Beijing Tianxue Network Education Technology Co. Ltd Beijing China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

Optical flow estimation is an essential task in the field of computer vision, aiming to capture the motion information of objects in image sequences over time. Optical flow estimation in traditional methods is typically modeled as an optimization problem, requiring the minimization of pixel-level brightness differences to compute optical flow, resulting in high computational complexity and demanding computational resources. With the advancement of deep learning technology, optical flow estimation has seen new breakthroughs. Deep learning models, usually based on Convolutional Neural Networks (CNNs), capture image features and motion patterns by learning from large-scale image data, thus achieving accurate optical flow estimation. Training deep learning models typically requires a large amount of labeled data, which can be difficult and expensive to obtain in the field of optical flow estimation. Therefore, on the one hand, we introduce an unsupervised optical flow estimation method based on photometric error design, which comprehensively considers pixel-level brightness differences, Structural Similarity Index (SSIM), and smoothness constraints. This method facilitates effective modeling and learning of optical flow estimation tasks in the absence of annotated data, enabling adaptation to various scenarios and data types, and exhibiting better generalization capability. On the other hand, we utilize short-term dense concatenate(STDC) modules to construct a feature encoding network, extracting multi-scale features with fewer parameters while reducing computational complexity.

关键词： Deep learning computer vision Accuracy Computational modeling Brightness Estimation Feature extraction Data models Computational complexity Optical flow

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Realtime Mask Detection of Kitchen Staff Using YOLOv5 and Edge Computing

Realtime Mask Detection of Kitchen Staff Using YOLOv5 and Ed...

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computer, control and Robotics (ICCCR), international conference on

作者： Yunfan Shi Zheng Yang Yifei Bi Jingcheng Li Xiaohui Zhu Yong Yue Department Of Computer Science The University of Liverpool Liverpool UK School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China

We propose a new end-to-end, edge-device embedded solution for detection of improper mask-wearing of kitchen staff using network cameras. Mask detection on kitchen network cameras is a highly specified task with little ready-to use data. The filming position of network cameras and the enviroment in kitchen leaded to vague images and small targets. Also, the detection model should be light-weighted to be deployed on edge computing devices. To improve accuracy on this task, We constructed a novel dataset from real kitchen cameras of different positions, indoor layouts, light conditions and applied effective data augementation. We conducted transfer learning on our dataset starting from COCO pre-trained YOLOV5s weights. In addition, we optimized the model through parameter tuning and post training model pruning for a single Nvidia Jetson Nano device and achieved high accuracy and sensitivity on multiple HD resolution network camera video feed. Experimental results show that our model achieves a training accuracy of 100 percent mAP(0.5) and test accuracy of 97.6 percent mAP(0.5) with minimal training cost: only 89 epochs on our dataset with early stopping. With an inference speed of 10 FPS on Nvidia Jetson Nano, our solution suffices the application requirements and can handle mutiple parallel HD camera streams simultaneously. Compared with previous research, our solution provides competitive cost efficiency where accurate and sensitive high resolution image detection can be run on a single edge device.

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Human motion recognition using three-dimensional skeleton model based on RGBD vision system 3

Human motion recognition using three-dimensional skeleton mo...

引用

2020 3rd international Symposium on Power Electronics and control Engineering, ISPECE 2020

作者： Ye, Qing Tan, Zexian Qu, Chang Zhang, Li School of Computer Science and Technology North China University of Technology Beijing100144 China

Human action recognition is one of the most important topics in computer vision. It has a lot of applications in intelligent monitoring, human-computer interaction, virtual reality, motion analysis and video annotation, etc. However, the effects of recognition are usually affected by human occlusion, self-occlusion, ambiguity and so on. In view of the above problems, this article describes an algorithm to recognize human motion using three-dimensional skeleton model based on RGBD vision system. Firstly, the human body three-dimensional skeleton model is established with the Kinect. Next, the normalized relative positions of skeleton joints and joint angles are adopted as features, and the action database containing the color image, the depth image, the skeletal image and the coordinates of the joints is built. Then, the features are trained and classified by multi-class support vector machine classifier. Finally, human motion recognitions are carried out. Experimental results indicate that the algorithm can recognize 12 kinds of actions behaved by different humans effectively. © Published under licence by IOP Publishing Ltd.

关键词： Three dimensional computer graphics

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Cloud Cost City: A Visualization of Cloud Costs using the City Metaphor 16

Cloud Cost City: A Visualization of Cloud Costs using the Ci...

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16th international Joint conference on computer vision, Imaging and computer Graphics Theory and Applications (VISIGRAPP) / 12th international conference on Information Visualization Theory and Applications (IVAPP)

作者： Dashuber, Veronika Philippsen, Michael QAware GmbH Aschauer Str 32 Munich Germany Friedrich Alexander Univ Erlangen Nurnberg FAU Programming Syst Grp Martensstr 3 Erlangen Germany

ISBN: (纸本)9789897584886

Many companies are in the process of migrating their entire IT infrastructure into the cloud in order to benefit from its high elasticity and scalability. While cloud providers offer basic cost visualizations such as line, bar or pie charts, companies lose track of which part of their infrastructure causes which costs. We adapt the city metaphor to visualize both the architecture and the costs. We offer a flexible framework to structure and tailor the visualization as desired. Using the Goal-Question-Metric approach, we identify cost savings potential and demonstrate for an example cloud infrastructure that the defined metrics are easier to grasp in our visualization compared to a standard cost dashboard.

关键词： Cloud Infrastructure Cloud Costs City Metaphor Infrastructure Visualization Cost Visualization

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Exploring the Role of Generative Adversarial Networks (GANs) in Image Translation using Load Balancer Interface Application

Exploring the Role of Generative Adversarial Networks (GANs)...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： T A Mohanaprakash Mythili Nagalingam T Tamilarasan R. Bhavani V. Gokula Krishnan Department of Computer Science and Engineering Panimalar Engineeering College Chennai India Department of Computer Science and Engineering St. Joseph’s Institute of Technology OMR Chennai Department of Computer Science and Engineering Chennai Institute of TechnologyKundrathur Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai Tamil Nadu India

Generative Adversarial Networks (GANs) play a crucial role in the dynamic fields of artificial intelligence and computer vision, notably impacting image-to-image translation. This study conducts a thorough examination of GANs’ application in image translation, providing a detailed literature review with a focus on contextual relevance. The clarification of acronyms in the keywords enhances reader comprehension. Beyond critically analyzing the limitations of previous research, the study explores strategies for enhancing the performance and accuracy of GANs in image translation. It offers a comprehensive exploration of fundamental GAN concepts, tracing their development over time, including the minimax game, adversarial training process, generator and discriminator networks, and underlying mathematical principles such as discriminator loss and GAN loss functions. The study investigates the origins of GANs, acknowledging pivotal discoveries and contributions, notably crediting Ian Goodfellow and lab researchers for their invention. This research aims to deepen understanding of GANs’ role in image translation, providing insights into their evolution, essential concepts, and potential avenues for improvement. Furthermore, experiments show that the suggested method is resistant against a variety of geometric transformations, such as translation, rotation, and combinations of these effects. These experiments are assessed using metrics like as PSNR, SSIM, incep, and FID. In contrast to the state-of-the-art super-resolution (SR) techniques now in use, the suggested method exhibits better performance on a variety of publically accessible datasets that are frequently used in research communities.

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Autonomous Parking Space Detection for Electric Vehicles Based on Advanced Custom YOLOv5

Autonomous Parking Space Detection for Electric Vehicles Bas...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Deepa Balusamy Geetha Shanmugam Baazilkumar Rameshkumar Agathiyan Palanisamy Giftson Kingston Logeshraj Ravi Department of Computer Science and Engineering Nandha Engineering College Erode India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

This research contributes to the development and deployment of smart parking system utilizing cutting-edge computer vision techniques. The study utilizes a subset of the Google Open Images Dataset focused on parking spaces to train a Custom Object Detection model tailored for low-computation devices. Employing YOLOv5 architecture and the Feature Pyramid Network (FPN), the system effectively detects parking spaces and objects within them. Utilizing ResNetv250 as the backbone architecture ensures precise feature extraction, resulting in high accuracy in identifying parking spots. Experimental results reveal the efficacy of the envisioned methodology, with Efficient YOLOv5 model achieving a mean Average Precision (mAP) of 0.970 for object detection in aerial dimensional frames. Graphical representations of F1-Confidence Curve and Precision-Confidence Curve demonstrate the model’s robustness in balancing precision and recall, even with an imbalanced dataset. This research underscores the potential of the proposed smart parking system to revolutionize urban parking management, providing an efficient and accurate solution for parking space identification.

关键词： YOLO Space vehicles Accuracy Urban areas computer architecture Feature extraction Electric vehicles

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Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Translation 18

Diagonal Attention and Style-based GAN for Content-Style Dis...

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18th IEEE/CVF international conference on computer vision (ICCV)

作者： Kwon, Gihyun Ye, Jong Chul Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Daejeon South Korea Korea Adv Inst Sci & Technol Grad Sch AI Daejeon South Korea

ISBN: (纸本)9781665428125

One of the important research topics in image generative models is to disentangle the spatial contents and styles for their separate control. Although StyleGAN can generate content feature vectors from random noises, the resulting spatial content control is primarily intended for minor spatial variations, and the disentanglement of global content and styles is by no means complete. Inspired by a mathematical understanding of normalization and attention, here we present a novel hierarchical adaptive Diagonal spatial ATtention (DAT) layers to separately manipulate the spatial contents from styles in a hierarchical manner. Using DAT and AdaIN, our method enables coarse-to-fine level disentanglement of spatial contents and styles. In addition, our generator can be easily integrated into the GAN inversion framework so that the content and style of translated images from multi-domain image translation tasks can be flexibly controlled. By using various datasets, we confirm that the proposed method not only outperforms the existing models in disentanglement scores, but also provides more flexible control over spatial features in the generated images.

关键词： computer vision

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