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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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Theorized Work for Adapting the Principles of Animation to Virtual Reality as a New Form of Narrative for 3D Animation 16

Theorized Work for Adapting the Principles of Animation to V...

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16th international Joint conference on computer vision, Imaging and computer Graphics Theory and Applications (VISIGRAPP) / 16th international conference on computer Graphics Theory and Applications (GRAPP)

作者： Salomao, Andre Horn Vieira, Milton Luiz Romeiro, Nicolas C. Nassar, Victor Univ Fed Santa Catarina DesignLab R Roberto Sampaio Gonzaga 274 Florianopolis SC Brazil

ISBN: (纸本)9789897584886

In this paper, we will for starters, introduce the basics concepts of virtual reality, how it works, which its upsides and downsides are, followed by the contextualization of the twelve principles of animation then we open a discussion about how we can adapt it for the media of virtual reality. The goal of this research is to create a discussion about the utilization of virtual reality as a new form of narrative for third-dimensional animation and how we can improve the adaptation of standard animation concepts to a new form of media.

关键词： Virtual Reality 3D Animation Principles of Animation

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Design and Implementation of Intelligent Lock control System for Data Center Based on Biometrics

Design and Implementation of Intelligent Lock Control System...

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Data Science and computer Application (ICDSCA), IEEE international conference on

作者： Fan Ding Junwei Wang Chunliang Yao Xiangwan Huang Ningbo Shengjiu Technology Co. Ltd. Ningbo China

In order to meet the requirements of integrated management of electronic lock with high security and high convenience in application scenarios such as data center network cabinet, a lock control system based on biometric technology is designed, which integrates electronic lock, network controller, collector and lock control platform. Platform management is used for system integration. By constructing dual MCU system of electronic lock ESP32-C3FH4 and network controller CH32V307VCT6, and integrating system data into biometric electronic lock, electronic lock adopts modular design to meet different functional requirements. The lock-control system realizes face and fingerprint template transmission, authentication information and event management through wireless and wired network communication. The existing Modbus TCP communication protocol is upgraded, and MODBUS protocol is extended to UDP communication, so as to realize the transmission of large throughput data. Through the test of the lock-control network management platform, the network controller can realize the centralized management of 8 lock information, and realize the authentication and permission change of operation and maintenance personnel through the collection and uploading of biometric information such as face and fingerprint. The test face module prosthesis resistance rate of 100%, face in vivo detection reception rate of 99.71%, from trigger to single face, fingerprint recognition, unlocking time is less than 2.5 seconds. The system expands the application of biometric electronic lock in industrial field.

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Mask Wearing Detection System Based On YOLO And Raspberry Pi

Mask Wearing Detection System Based On YOLO And Raspberry Pi

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Ruishi Liang Yizhu Chen Shuaibing Li Huizhi Yang School of Computer University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China

To identify mask wearing quickly and automatically in public places is particularly important for epidemic prevention and control. In this paper, we present a real-time mask wearing detection algorithm based on improved YOLOv5s, which speeds up the reasoning speed by 5~10% and achieves a detection accuracy of more than 96%. The proposed algorithm can be easily deployed in Raspberry PI. We also design a Web-based mask wearing detection system consisting of two parts: the cloud subsystem and the edge subsystem. The cloud part mainly realizes data storage, model training, equipment monitoring, big data visualization and other functions. The edge part uses Raspberry Pie as the core deployment equipment to complete data collection, model reasoning, information early warning and other functions. Our system features the advantages of high real-time, low cost and low network traffic. It can be widely deployed in the supermarket, parks, intelligent light poles and other open scenes, resulting in greater practical application value.

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Intensifying Practical Based Learning of Penetration Testing using CTF 3

Intensifying Practical Based Learning of Penetration Testing...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Sharma, Sugandha Kumar, Rakesh Jain, Amit Agarwal, Bharat Suman, Sujay Kumar Chandigarh University Dept. of CSE Mohali India Chandigarh University Dept. of Computer Science and Engineering Mohali India

ISBN: (纸本)9781665438117

In a world where we cannot imagine even a single day without a computer, we also need to understand that everyday many unauthorized criminals find exploits in those computers and networks and not only cause disruption in services but also cost huge amount of monetary losses and sometimes demand for ransom. To prevent such security risks, we need people who understand these vulnerabilities and flaws in any system, people who can think like attackers. To find these experts many organizations conduct different types of tests, contests, and interviews. These platforms who conduct such contests are called CTF's (Capture the Flag) where candidates must find the vulnerabilities and flags in a buggy/vulnerable system. There are flags and checkpoints by problem setter which needs to be submitted by the contestant. Conventionally, the shortlisted candidates are then invited for an interview about their procedure and method of finding the vulnerability. This adds an extra step and increases cost due to human engagement. We aim to develop a better CTF platform where various upgrades like more accurate leaderboard with better score calculation formula and ways to reduce and simplify selection procedure by introducing features such as write-ups. © 2021 IEEE.

关键词： Cybersecurity

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A Generalized Grayscale Image Processing Framework for Retinal Fundus Images

A Generalized Grayscale Image Processing Framework for Retin...

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Intelligent Technologies (CONIT), international conference on

作者： Siddhesh Yerramneni Kotta Sai Vara Nitya Sirikrishna Nalluri Rajiv Senapati Dept. of Computer Science & Engineering SRM University Andhra Pradesh India

Diabetic Retinopathy (DR) is a debilitating ocular complication of diabetes that results from prolonged exposure of the retina to elevated levels of blood glucose. This exposure can lead to progressive microvascular changes and neuronal injury, resulting in a spectrum of visual impairments ranging from mild vision changes to severe vision loss and blindness. DR typically manifests as structural changes in the blood vessels of the retina, including capillary non-perfusion, microaneurysms, retinal hemorrhages, and new vessel formation. DR is challenging to diagnose and treat due to the gradual onset of symptoms and the lack of early warning signs. Therefore, regular eye exams are critical for early detection and management of DR. A human ophthalmologist would take a significant amount of time, based on their ability and experience, to go through the fundus image and diagnose DR. Despite advancements in DR management, it remains a significant public health issue, and further research is essential to improve the understanding of DR in order to overcome the existing complications. This paper proposes a solution for improving retinal fundus images by creating more precise computerized image analysis medical diagnosis with fewer computational requirements as the images are grayscaled so that irrespective of the imaging apparatus the features of the images are enhanced without loss of information. The results of the proposed framework are assessed using entropy, contrast improvement index and structural similarity index measure.

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Real-time Face Mask Detection Using Deep Learning on Embedded Systems 3

Real-time Face Mask Detection Using Deep Learning on Embedde...

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3rd international conference on Electrical, control and Instrumentation Engineering (ICECIE)

作者： Lopez, Vidal Wyatt M. Abu, Patricia Angela R. Estuar, Ma Regina Justina E. Ateneo Manila Univ Dept Informat Syst & Comp Sci Quezon City Philippines

ISBN: (纸本)9781665449663

Coronavirus disease (COVID-19) is an infectious disease, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that was identified in December 2019 in Wuhan, China [1], [2]. It is a pandemic that causes respiratory disorder and is transmitted through sneezing droplets of infected individuals. These droplets can fall on the objects around the effected and enter a healthy individual through contact. Major symptoms of this disease include lethargy, dry cough, followed by fever [3]. The number of cases is surging dramatically, raping developed and undeveloped countries together [3]. According to the World Health Organization (WHO) COVID-19 weekly epidemiological Update for 29(th) of December there are 79 million infected cases and 1.7 million deaths *** pandemic not only affects our health but also affects our livelihood. In the absence of specific treatment or a vaccine, non-pharmaceutical interventions (NPI) form the backbone of the response to the COVID-19 pandemic. These NPI includes physical distancing, regular hand washing, and wearing a face mask. This study aims to help with the monitoring of these NPIs specifically wearing face masks using deep learning. This study implements face mask detection and recognition system that automatically detects and recognizes if a person is wearing a Medically approved face mask, Non-Medically approved face mask, or not wearing a mask at all. This study has determined that MobileNetV1 model has shown the best performance regarding classification (79%) and processing speed up to 3.25 fps.

关键词： Face mask detection embedded systems computer vision convolutional neural network

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Data-driven Model for Indoor Temperature Prediction in HVAC-Supported Buildings

Data-driven Model for Indoor Temperature Prediction in HVAC-...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Darko Palaić Iva Matetić Sandi Ljubic Ivan Štajduhar Igor Wolf Department of Thermodynamics and Energy Engineering Faculty of Engineering University of Rijeka Rijeka Croatia Department of Computer Engineering Faculty of Engineering University of Rijeka Rijeka Croatia

Increasing energy demand is an important issue due to limited resources and environmental concerns. Buildings are responsible for a large portion of total energy consumption, with heating, ventilation, and air conditioning (HVAC) systems being the largest consumer. With the advent of internet of things (IoT) technology, a large amount of data can be collected from buildings, providing insights into the operation of HVAC systems. This data can be employed with data-driven methods to improve efficiency, analyze performance, and develop models that represent system behavior. In this study, a data-driven artificial neural network model for predicting indoor temperatures in commercial buildings is presented. The data preprocessing procedure is described and the main features used to build the model are identified. The results show that the data-driven model can predict indoor temperatures with an intraday root mean squared error (RMSE) of 0.85 °C. The developed model has the potential to be integrated into a predictive control system as a possible solution to reduce energy consumption.

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Multimodal Brain Tumor Segmentation Using a Dual-Branch vision Transformer Integrated with Region-Attention Fusion Network

Multimodal Brain Tumor Segmentation Using a Dual-Branch Visi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Sunita Yadwad Rajeswari C D. Nirmal Raj Pravin Dange Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur India Balaji Institute of Management and Human Resource Development (BIMHRD) Pune India Symbiosis Institute of Management Studies Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Accurate segmentation of brain tumors is a critical task in medical imaging, aiding diagnosis, treatment planning, and prognosis. This paper introduces a novel framework for brain tumor segmentation that leverages a Dual-Branch vision Transformer (DB-ViT) integrated with a Region-Attention Fusion Network (RAFN) to address the limitations of conventional convolutional neural networks. The DB-ViT processes multimodal MRI sequences, including T1, T1c, T2, and FLAIR, to extract global contextual features while preserving modality-specific information. Simultaneously, the RAFN enhances segmentation precision by refining local features and focusing on critical regions of interest through an attention mechanism. The proposed method effectively combines global and local feature representations, ensuring accurate delineation of tumor boundaries. Extensive experiments were conducted on the BraTS dataset, demonstrating the superiority of our approach over existing methods such as U-Net, Attention U-Net, and TransUNet. Quantitative evaluation metrics, including Dice Score and Intersection over Union (IoU), indicate significant improvements in segmentation accuracy. The framework also achieves computational efficiency and robustness across diverse tumor morphologies. Visual results further validate its ability to enhance boundary delineation, particularly in heterogeneous tumor regions. The proposed methodology represents a significant advancement in automated medical image analysis, offering potential for real-time clinical applications. Future work aims to explore broader clinical validation and real-time deployment of this framework.

关键词： Measurement Image segmentation computer vision Accuracy Attention mechanisms Refining Brain tumors Transformers Real-time systems Planning

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VR Serious Game for Learning the computer Organisation and Architecture Course

VR Serious Game for Learning the Computer Organisation and A...

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Educational Technology (ICET), IEEE international conference on

作者： Kai Hwee Alvin Tan Yiyu Cai Bheema Thiagarajan Lokesh Yongqing Zhu Chengzhi Su Qi Cao School of Science and Technology Singapore University of Social Sciences Singapore School of Mechanical & Aerospace Engineering Nanyang Technological University Singapore School of Artificial Intelligence Changchun University of Science and Technology Changchun China School of Computing Science University of Glasgow UK/Singapore

computer Organisation and Architecture is a core subject in Computing Science (CS) in Higher Education. Some topics in this subject are thought to be dry by students through traditional teaching, such as computer functions and interrupts, as students are unable to visualize these multiple-step procedures of how the CPU performs. This research aims to utilize the virtual reality (VR) serious game to help students learn better these topics and absorb abstract concepts or comprehend scenarios that would be difficult in a typical classroom setting. It also enables an engaging and motivating platform, achieving successful learning for CS students. The effectiveness of the designed VR serious game is evaluated with two groups of participants: control group and experiment group. The results reveal that the serious game is able to provide students with positive learning efficiency as well as an engaging and self-motivated experience. The research provides an effective way of teaching and learning modality of the computer Organisation and Architecture subject for CS students.

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Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic Segmentation 33

Pseudo-Label Noise Suppression Techniques for Semi-Supervise...

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33rd British Machine vision conference proceedings, BMVC 2022

作者： Scherer, Sebastian Schön, Robin Lienhart, Rainer University of Augsburg Machine Learning and Computer Vision Lab Augsburg Germany

Semi-supervised learning (SSL) can reduce the need for large labelled datasets by incorporating unlabelled data into the training. This is particularly interesting for semantic segmentation, where labelling data is very costly and time-consuming. Current SSL approaches use an initially supervised trained model to generate predictions for unlabelled images, called pseudo-labels, which are subsequently used for training a new model from scratch. Since the predictions usually do not come from an error-free neural network, they are naturally full of errors. However, training with partially incorrect labels often reduce the final model performance. Thus, it is crucial to manage errors/noise of pseudo-labels wisely. In this work, we use three mechanisms to control pseudo-label noise and errors: (1) We construct a solid base framework by mixing images with cow-patterns on unlabelled images to reduce the negative impact of wrong pseudo-labels. Nevertheless, wrong pseudo-labels still have a negative impact on the performance. Therefore, (2) we propose a simple and effective loss weighting scheme for pseudo-labels defined by the feedback of the model trained on these pseudo-labels. This allows us to soft-weight the pseudo-label training examples based on their determined confidence score during training. (3) We also study the common practice to ignore pseudo-labels with low confidence and empirically analyse the influence and effect of pseudo-labels with different confidence ranges on SSL and the contribution of pseudo-label filtering to the achievable performance gains. We show that our method performs superior to state of-the-art alternatives on various datasets. Furthermore, we show that our findings also transfer to other tasks such as human pose estimation. Our code is available at https://***/ChristmasFan/SSL_Denoising_Segmentation. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms

关键词： Semantics

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