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Enhanced Video Streaming Based Computing Encoder for Digital visual processing

Enhanced Video Streaming Based Computing Encoder for Digital...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), ieee International conference on

作者： Tarun Kumar Amita Shukla Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Business System Noida Institute of Engineering and Technology Greater Noida India

The rise of video streaming for digital visual processing has been a boon for the industry of visual processing. Video streaming technology has made it easier for companies to capture, analyze, and interpret visual data faster than ever before. It has allowed for the storage and transmission of large amounts of visual data at high speeds, providing businesses with the ability to process and interpret this data in real-time. Video streaming technology can be used in a wide variety of applications, including facial recognition, 3D mapping, and object recognition. By streaming video data, companies can quickly and accurately identify individuals, recognize objects, and track movement. This technology can also be used in security applications, such as surveillance and monitoring, as well as in medical imaging, such as MRI and CT scans. Video streaming technology has also allowed companies to create more efficient visual processing systems. The streaming video data can be used to automate the process of image recognition and object classification. This has allowed companies to reduce the amount of time and effort needed to interpret visual data. Additionally, streaming video data can be used to create virtual reality experiences, providing users with an immersive experience when viewing digital images.

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CLSD: Continual Learning for Lane Line Segmentation Across Domains 7

CLSD: Continual Learning for Lane Line Segmentation Across D...

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ieee 7th International conference on Intelligent Transportation Engineering (ICITE)

作者： Wu, Yan Liao, Yujun Jiang, Wei Zhao, Junqiao Liu, Feilin Mo, Yujian Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Tongji Univ Inst Intelligent Vehicle Shanghai Peoples R China

ISBN: (纸本)9781665460071

Lane line segmentation is a fundamental task in the field of autonomous driving, and it's intuitive that automatic vehicles should have the ability of processing data from different domains. The usual assumption is that training data of all the domains is available at a time, however, it's more common to collect data continually and train the visual system incrementally. To date, many works focus on segmenting lane line in a single domain, but no work considers continually segmenting lane line across domains. To tackle with this task, we presents a method, named as CLSD1 which leverages data from new domain while avoiding catastrophic forgetting. The core of CLSD are a training framework that effectively transfer knowledge from the previous model to the new model and a frustrating simple yet useful self-training strategy. We conduct extensive experiments based on TSD datasets and showcase that on daytime our training framework increases the miou by 10% on the fog domain data, and the self-straining increases the miou by 11% on the fog domain data and 47% in night domain data.

关键词： Catastrophic forgetting image transformation transfer learning self-training diversified scene domains

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Development of Contactless Employee Management System with Mask Detection and Body Temperature Measurement using TensorFlow 6

Development of Contactless Employee Management System with M...

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6th International conference on Wireless communications, Signal processing and Networking (ieee WiSPNET)

作者： Ulleri, Pranesh Mukund, S. Sudarshan, K. Zenith, Kiran Shibu, Sai N. B. Amrita Vishwa Vidyapeetham Dept Comp Sci Engn CSE Amritapuri India Amrita Vishwa Vidyapeetham Ctr Wireless Networks & Applicat WNA Amritapuri India

ISBN: (纸本)9781665440868

CoVID-19 pandemic signified the importance of social distancing and non-contact routines. Even after the pandemic, everyone should abide by the same protocols for a hygienic life. Institutions must put in place pre-emptive measures before people return. These include marking the presence of employees and monitoring their health status. This paper focuses on developing a contactless employee management system with sensor fusion and facial recognition technology. image processing capability in the system track employees entering the institution. Body temperature sensor collect the health status of each employee at the entrance. With the onboard cameras and deep learning algorithms, the system also makes sure if the employee is wearing a mask or not. The aim is to authenticate an employee and check whether they abide by the protocols of the institution. Ease of use, maintenance, and low-cost installation is the motivation behind the system design. The best beneficiaries are educational institutions and corporate offices. The system records employee health data and uses it for the contact tracing. This paper discusses the system design and algorithms of the non-invasive employee management system.

关键词： Raspberry Pi Deep Learning image processing Facial Recognition CoVID Protocol

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Imperceptible and Targeted Physical Attacks on Deep Learning-Based Speech Semantic communications

Imperceptible and Targeted Physical Attacks on Deep Learning...

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ieee conference on Wireless communications and Networking

作者： Yuhao Hua Yang Xu Chen Lyu Jia Liu Yulong Shen Weidong Yang Norio Shiratori Hangzhou Institute of Technology Xidian University Hangzhou China School of Computer Science and Technology Xidian University Xi'an China MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China Center for Strategic Cyber Resilience Research and Development National Institute of Informatics Tokyo Japan Research and Development Initiative Chuo University Tokyo Japan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The deep learning-based semantic communication system (DeepSC) is designed to improve the efficiency and accuracy of information transmission, by leveraging joint source-channel coding techniques to extract relevant semantic features. However, existing research on attack methods targeting DeepSC has primarily focused on text and image domains, leaving the speech domain largely unexplored. To this end, this paper proposes Iterative Semantic Gradient Update (ISGU), a novel approach for crafting physical layer adversarial attacks on DeepSC for speech transmission (DeepSC-ST). Specifically, we introduce a joint loss that combines the semantic similarity loss with Connectionist Temporal Classification loss to expedite the generation process of targeted attacks against the DeepSC-ST. In addition, we design an algorithm to generate adversarial examples, enhancing their imperceptibility by meticulously controlling the perturbation power added to the input speech. Extensive experiments indicate that ISGU is capable of rapidly generating highly covert adversarial examples, with a notably high attack success rate.

关键词： Training Perturbation methods Oral communication Information processing Speech enhancement Semantic communication Physical layer Feature extraction Security Iterative methods

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Tone and image processing in Graphic Design based on De-Mosaic Technology 2

Tone and Image Processing in Graphic Design based on De-Mosa...

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2nd International conference on Smart Electronics and Communication, ICOSEC 2021

作者： Wu, Zhen Suzhou Vocational University JiangSu SuZhou215000 China

ISBN: (纸本)9781665433686

The field of visual communication includes a wide range of design. Besides logo design, it is also popular to use graphic flat design, packaging design, poster design, illustration design and so on. Based on the diversification of new media and the rapid development of the Internet, visual communication has gradually emerged some new characteristics of the times. This paper designs a joint image mosaic denoising method based on deep convolution network, and also utilizes the network to correct the learned mapping relationship. The experimental results show that this method can obtain color images with rich texture and good visual effect. © 2021 ieee.

关键词： visual communication

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A Super-Resolution Technique with An Ensemble Deep Learning Based for Cervical Cancer Detection System

A Super-Resolution Technique with An Ensemble Deep Learning ...

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2022 ieee North Karnataka Subsection Flagship International conference, NKCon 2022

作者： Murugan, K. Gomathy, B. Prasath, N. Umaamaheshvari, A. Malathi, D. KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Dr.NGP Institute of Technology Department of Computer Science and Engineering Coimbatore India SRM Institute of Science and Technology Department of Networking and Communications Chennai India Akshaya College of Engineering and Technology Department of Electronics and Communication Engineering India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9781665453424

A novel way for classifying cervical cells using Convolutional Neural Networks (CNN) is presented in this work. Features are learned automatically in this technique. Classification accuracy and model diversity can be enhanced using ensemble of CNN. This is a multi-step process and it requires the training of various CNN models and they must selected accordingly for ensemble. So, for resolving these issues and for creating expert medical decision making systems, Convolutional Neural Network and Random forest with Boosting called CNNRFB is used in current research work. For cervical cancer Prediction, an optimal prediction model can be constructed by medical practitioners using this solution. One dataset with all relevant information about various cervical cancer is created initially. In an image, without information loss, image pre-processing is done using super resolution technique. Self-similarity based Non-Local-Mean (NLM) metric is used as a base for this Super-Resolution (SR) algorithm. The NLM weight is used as a self-similarity metric under noisy environment for computing best self-example accurately. Deep ensemble learning methods are used for performing image classification and cancer prediction. System's accuracy are decided by dataset values and deep learning training technique. Python code is used for executing and simulating all the techniques. The experimental results shows the accuracy, recall, f-measure and precision produced by proposed technique shows better results when compared with the traditional approches. © 2022 ieee.

关键词： Convolutional neural networks

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Vision-and-Dialog Navigation by Fusing Cross-modal features

Vision-and-Dialog Navigation by Fusing Cross-modal features

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International Joint conference on Neural Networks (IJCNN)

作者： Nie, Hongxu Dong, Min Bi, Sheng South China Univ Technol Guangzhou Peoples R China

ISBN: (纸本)9780738133669

Recently, research in robotics community shows a trend that the future intelligent robots should be endowed with the capacities to understand the environment and navigate to the goal location through the communications with their human users. Such tasks usually require the smart agents to process multi-modal information effectively. Though multi-modal information processing has been long studied, the problem of how to effectively fuse different modalities of information remains challenging. In this paper, we focus on the vision-and-dialog navigation(NDH) task. The NDH task is proposed for building dialog-enabled agents which can find a path to the goal location in unexplored environments by inferring navigation actions based on the dialog history and the visual inputs. We first investigate the problem about what role the visual features are playing in NDH task. We observe the same trend which was observed in the vision-and-language Navigation(VLN) task. The conclusion is using different levels of visual features affects the model performance seriously. Particularly, using low-level visual features makes the agent models hardly generalize in unseen environments (i.e., environments not used in training). Models using only high-level visual features perform better in unseen environments. However, these models suffer a significant performance drop in seen environments, which means these models can not understand and remember the seen environments thoroughly. According to this observation, we explore several ways to fuse these features. We propose a model to fuse the dialog feature with each modality of visual feature. On the other hand, the prediction of our model is an ensemble of jointly-trained models which focus on different modalities. Our proposed method can be applied in any VLN models and NDH models. Our results show that our method can improve the performance of NDH models in both seen environments and unseen environments.

关键词： feature fusing multi-modal information processing vision-and-dialog embodied navigation

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Perceptual Hashing Using Pretrained Vision Transformers

Perceptual Hashing Using Pretrained Vision Transformers

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ieee Games Media Entertainment (GEM)

作者： Jelle De Geest Patrick De Smet Lucio Bonetto Peter Lambert Glenn Van Wallendael Hannes Mareen Department of Electronics and Information Systems IDLab Ghent University – imec Gent Belgium National Institute of Criminalistics and Criminology (NICC) Brussels Belgium

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

The rapid evolution of digital image circulation has necessitated robust techniques for image identification and comparison, particularly for sensitive applications such as detecting Child Sexual Abuse Material (CSAM) and preventing the spread of harmful content online. Traditional perceptual hashing methods, while useful, fall short when exposed to some common image transformations, or when images are doctored to avoid detection, rendering them ineffective for nuanced comparisons. Addressing this challenge, this paper introduces a novel pretrained vision transformer artificial intelligence (AI) model approach that enhances the robustness and accuracy of perceptual hashing. Leveraging a pretrained Vision Transformer (ViT-L/14), our approach integrates visual and textual data processing to generate feature arrays that represent perceptual image hashes. Through a comprehensive evaluation using a dataset of 50,000 images, we demonstrate that our method offers significant improvements in detecting similarities for certain complex image transformations, aligning more closely with human visual perception than conventional methods. While our method presents certain initial drawbacks such as larger hash sizes and high computational complexity, its ability to better handle perceptual nuances presents a forward step in the realm of image forensics. The potential applications of this research extend to law enforcement, digital media management, and the broader domain of content verification, setting the stage for more secure and efficient digital content analysis.

关键词： visualization image transformation image forensics Accuracy Media Transformers Robustness

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International Deployment of visual IoT for Disaster Mitigation

International Deployment of Visual IoT for Disaster Mitigati...

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Australasian Telecommunication Networks and Applications conference, ATNAC

作者： Ken T. Murata Kazutaka Kikuta Tsutomu Nagatsuma Hideo Imanaka Praphan Pavarangkoon National Institute of Information and Communications Technology Tokyo Japan School of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

This paper proposes a novel methodology that utilizes a newly developed visual Internet of Things (IoT) system for resilient natural disaster mitigation. This system enables the detection of disasters through remote control functions integrated with visual IoT sensors and artificial intelligence (AI)-based image processing of images captured by these sensors. The system is designed using commercial off-the-shelf (COTS) components to reduce installation costs, making it feasible for deployment worldwide, including in developing countries. In 2023, the proposal for this system was presented to the ITUD with the aim of widespread implementation in developing nations. The paper introduces innovative methodologies for visual IoT in international disaster mitigation, accompanied by use cases and detailed technological insights.

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6th International conference on Computer Vision and image processing, CVIP 2021

6th International Conference on Computer Vision and Image Pr...

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6th International conference on Computer Vision and image processing, CVIP 2021

ISBN: (纸本)9783031113451

The proceedings contain 97 papers. The special focus in this conference is on Computer Vision and image processing. The topics include: Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection;signature2Vec - An Algorithm for Reference Frame Agnostic Vectorization of Handwritten Signatures;leaf Segmentation and Counting for Phenotyping of Rosette Plants Using Xception-style U-Net and Watershed Algorithm;fast and Secure Video Encryption Using Divide-and-Conquer and Logistic Tent Infinite Collapse Chaotic Map;visual Localization Using Capsule Networks;detection of Cataract from Fundus images Using Deep Transfer Learning;brain Tumour Segmentation Using Convolution Neural Network;signature Based Authentication: A Multi-label Classification Approach to Detect the Language and Forged Sample in Signature;a Data-Set and a Real-Time Method for Detection of Pointing Gesture from Depth images;Deep-TDRS: An Integrated System for Handwritten Text Detection-Recognition and Conversion to Speech Using Deep Learning;VISION HELPER: CNN Based Real Time Navigator for the visually Impaired;structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated images;low Cost Embedded Vision System for Location and Tracking of a Color Object;towards Label-Free Few-Shot Learning: How Far Can We Go?;AB-net: Adult- Baby Net;Polarimetric SAR Classification: Fast Learning with k-Maximum Likelihood Estimator;Leveraging Discriminative Cues for Masked Face Recognition in Post COVID World;pretreatment Identification of Oral Leukoplakia and Oral Erythroplakia Metastasis Using Deep Learning Neural Networks;soft Biometric Based Person Retrieval for Burglary Investigation;a Deep Learning Framework for the Classification of Lung Diseases Using Chest X-Ray images;computer Aided Diagnosis of Autism Spectrum Disorder Based on Thermal Imaging;scene Graph Generation with Geometric Context;deep Color Spaces for Fingerphoto Presentation Attack Detection in Mobile Devices;can

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