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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Yunsheng Deng Yinghui Huang Zhangbao Chen Yan Chen Bengbu University Bengbu Huainan City Anhui China

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

In today’s world, technology is changing our way of life and work at an alarming rate. This paper studies the performance of an improved deep learning algorithm in image processing tasks, introduces the implementation principle of algorithm design, and puts forward an improved deep learning algorithm. In the experimental method part, a group of experiments are designed to evaluate the image quality performance of the improved deep learning algorithm, which is based on three key performance indicators: Peak Signal-to-Noise Ratio (PSNR), Interactive Response time (IRT) and Structural Similarity Index Measure (SSIM). The research conclusion shows that the peak signal-to-noise ratio (PSNR) of the improved image quality-preserving deep learning algorithm is as high as 58 dB. The maximum IRT measurement of the improved algorithm is only 95 ms, which provides users with faster response speed and enables users to experience a smoother interactive experience in real-time image processing applications.

关键词： deep learning image quality Visualization PSNR image processing Scalability real-time systems time measurement Robustness time factors

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image processing based image to Cartoon Generation: Reducing complexity of large computation arising from deep learning

Image Processing based Image to Cartoon Generation: Reducing...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Shrivastava, Shruti Kumari Gajjar, Ruchi Institute of Technology Nirma University Electronics and Communication Engineering Department Gujarat Ahmedabad382481 India

ISBN: (纸本)9798350323917

This paper proposes an approach to convert real life images into cartoon images using image processing. The cartoon images have sharp edges, reduced colour quantity compared to the original image, and smooth colour regions. With the rapid advancement in artificial intelligence, recently deep learning methods have been developed for image to cartoon generation. Most of these methods perform extremely huge computations and require large datasets and are time consuming, unlike traditional image processing which involves direct manipulation on the input images. In this paper, we have developed an image processing based method for image to cartoon generation. Here, we perform parallel operations of enhancing the edges and quantizing the colour. The edges are extracted and dilated to highlight them in the output colour image. For colour quantization, the colours are assigned based on proposed formulation on separate colour channels. Later, these images are combined and the highlighted edges are added to generate the cartoon image. The generated images are compared with existing image processing approaches and deep learning based methods. From the experimental results, it is evident that the proposed approach generates high quality cartoon images which are visually appealing, have superior contrast and are able to preserve the contextual information at lower comnutational cost. © 2023 IEEE.

关键词： Median filters

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Enhanced image processing for Automobile Accident Detection Using deep learning 3

Enhanced Image Processing for Automobile Accident Detection ...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Usharani, S. Dhanushkumar, J. Manju Bala, P. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning Villupuram India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350391565

Road safety can be creatively increased by utilizing systems for reporting and detecting accidents use the YOLO algorithm. Yolo, which stands for "You Only Look Once,"is a sophisticated object recognition system that can identify and pinpoint objects in live video streams. By identifying accidents and notifying emergency services, the system lowers the reaction times and increases the possibility of saving lives by utilizing the YOLO algorithm. The object detection and alarm systems are the two primary parts of the proposed system. The YOLO method is employed by the object recognition module to look for accidents in live video broadcasts. A module of accident photos was used to teach the system to accurately identify incidents. When an accident is discovered, an alert system is activated. The location of the accident and a brief account of what transpired are communicated to the emergency services by the alert system. This information is communicated to emergency services through a wireless communication network, which expedites response times and increases the likelihood of saving lives. Positive results were obtained by testing the system using the images and an accident module. It was shown that the warning system could react in a couple of seconds and that the YOLO algorithm could identify accidents having a precision of around 94. Highways, busy intersections, and other high-risk areas may have systems in place to increase traffic safety and lower the number of accidents. Developing crash detection and warning systems using deep learning using the YOLO technique is one way to potentially increase road safety. With the use of technology, problems may be precisely identified in real-time video feeds, alerting emergency services and potentially increasing survival rates and reaction times. © 2024 IEEE.

关键词： image enhancement

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Distributed deep learning for Medical image processing in Cloud Environments 1

Distributed Deep Learning for Medical Image Processing in Cl...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Varshney, Neeraj Madan, Parul Shrivastava, Anurag Kumar, C Praveen Rao, A Kakoli Srivastava, Amit Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The incorporation of distributed deep learning for medical image processing in cloud settings is the subject of this study. The findings demonstrate the high viability and significant performance advantages realized by cloud-based distributed systems, notably significant processing time savings, outstanding diagnostic accuracy, as well as improved scalability. The consequences for security and privacy have been discussed, with a focus on effective safeguards for private medical information. There is a void in the literature about resource and cost-effectiveness optimization tactics used in cloud-based systems. Future research must concentrate on resource optimization tactics for economic sustainability, study developing security risks and privacy techniques, and incorporate real-world implementations in order to improve this topic. This study informs the use of distributed deep learning in cloud-based medical image processing as well as adds to the body of knowledge in healthcare technology. © 2023 IEEE.

关键词： cloud computing Distributed deep learning medical image processing privacy resource optimization security

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Multi-modal active learning with deep reinforcement learning for target feature extraction in multi-media image processing applications

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MULtimeDIA TOOLS AND APPLICATIONS 2023年第4期82卷 5343-5367页

作者： Dhiman, Gaurav Kumar, A. Vignesh Nirmalan, R. Sujitha, S. Srihari, K. Yuvaraj, N. Arulprakash, P. Raja, R. Arshath Govt Bikram Coll Commerce Dept Comp Sci Patiala Punjab India Chandigarh Univ Univ Ctr Res & Dev Dept Comp Sci & Engn Mohali Punjab India Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India Jai Shriram Engn Coll Dept Comp Sci & Engn Tiruppur Tamil Nadu India Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Krishnankoil Tamil Nadu India Sri Vidya Coll Engn & Technol Dept Comp Sci & Engn Virudunagar Tamil Nadu India SNS Coll Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India ICT Acad Training & Res Chennai Tamil Nadu India Dept Comp Sci & Engn Rathinam Tech Campus Coimbatore 641021 Tamil Nadu India ICT Acad Res & Publicat IIT Madras Res Pk Chennai Tamil Nadu India

The advancement in on demand Multimedia Streaming Applications (MAS) enables faster video transmission as per the user request in various fields. This system suffers from poor speed, flexibility and efficiency in accessing and presenting the multimedia contents from the archive. It mostly undergoes delay, packet loss and congestion during data delivery. Hence, the requirement of manual annotation is required for access and retrieval but it suffers from poor retrieval accuracy over large databases. The need of automatic annotation in MAS takes the lead for increased retrieval accuracy on most similar image retrieval systems based on various low-level features. Thus, it eliminates the gap between the high-level semantics and low-level feature representation. The approach on automated annotation of images is considered dependent on the accuracy of a model while detecting edges, color, texture, shape and spatial information. In this paper, we develop an automated annotation model that retrieves visually similar images from online multimedia streams with optimal feature extraction. The automated annotation model is designed with a Multi-modal Active learning (MAL) that uses Convolutional Recurrent Neural Network (CRNN) for automatic annotation of labels based on visually similar contents or features like edges, color, texture, shape and spatial information. Further, a deep Reinforcement learning (DRL) algorithm is used that increases the performance of the retrieval engine based on validating the visually extracted features. The simulation of MAL-CNN is conducted over large online streaming databases and it is then validated by DRL on an online real-time streaming. The performance is validated in terms of its retrieval accuracy, sensitivity, specificity, f-measure, geometric mean and mean absolute percentage error (MAPE). The results confirm the accuracy of the proposed MAL-DRL model against conventional machine learning, reinforcement learning and deep learning automati

关键词： Multimodal active learning Convolutional neural network deep reinforcement learning Feature extraction Multimedia Streaming Systems

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deep learning Applications in Thermal IR image processing 1

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Hadi, Hayder Sabeeh Sabah, Hawraa Ali Obaid, Ahmed J. Zearah, Sajad Ali College of Nursing University of Kufa Najaf Iraq National University of Science and Technology Thi-Qar Iraq Faculty of Computer Science and Mathematics University of Kufa Kufa Iraq Scientific Research Center Al-Ayen University Thi-Qar Iraq

ISBN: (数字)9789819965472

ISBN: (纸本)9789819965465

IR image recognition has been a promising field for the past few years. However, it is difficult to identify facial emotions when it is dark, the lighting is poor, or there are other elements present. Thermal pictures are therefore recommended as a fix for these issues and for a number of additional advantages. Additionally, focusing on important areas of a face rather than the complete face is sufficient to reduce processing while also enhancing accuracy. This study provides brand-new infrared thermal image-based methods for identifying the images. The face’s whole image is first divided into four parts. Then, to create training and testing datasets, we only allowed four active regions (ARs). An approach to machine learning is called active neural network (ANN). We also used a parallelism strategy to speed up processing of training and testing datasets. We have observed a 46% reduction in processing time as a result. Finally, to increase the recognition accomplishes a recognition accuracy of 92.38%. The achieved accuracy validates the stability of our suggested strategy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： image recognition

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real-time Social Distance maintaining using image processing and deep learning

Real-Time Social Distance maintaining using Image Processing...

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2021 International Conference on Computing, Communication, Electrical and Biomedical Systems, ICCCEBS 2021

作者： Senthil Murugan, K.R. Kavinraj, G. Mohanaprasanth, K. Ragul, Krishnan B. Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Quite possibly the most and best measures to contain the new popular episode is that the upkeep of the purported Social Distancing. The widespread Covid infection 2019 (COVID-19) has carried worldwide emergency with its lethal spread to very 180 countries. This paper shows the procedure for social separating recognition utilizing profound figuring out how to check the space between individuals to relieve the effect of this Covid pandemic, this content proposes a profound learning based system for robotizing the assignment of observing social removing utilizing reconnaissance video. Revelation instrument was made to form people mindful of manage an ensured distance. An information from the camera source utilized as video graph, as necessities be the open-source object territory model maintained the YOLOv3 object divergence model to disconnect people from the inspiration and deep-sort thanks to affect oversee follow the apparent individuals with the assistance of bobbing boxes and dispatched IDs. The space between people is much of the time evaluated and any defiant pair of people inside the introduction are shown with red packaging and line. The future procedure was affirmed on a pre-recorded audiovisual of people by walking around and about. The outcome shows that the proposed strategy is in a situation to work out the social removing measures between various individuals inside the video. These system are consistently moreover developed as an ID mechanical assembly continuously application. © Published under licence by IOP Publishing Ltd.

关键词： deep learning

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real-time image processing and deep learning 2020

Real-Time Image Processing and Deep Learning 2020

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real-time image processing and deep learning 2020

ISBN: (纸本)9781510635791

The proceedings contain 12 papers. The topics discussed include: real-time detection of maize crop disease via a deep learning-based smartphone app;parallel artificial neural networks using wavelet-based features for classification of remote-sensing hyperspectral images;no-reference image quality assessment based on residual neural networks (ResNets);coverless image steganography framework using distance local binary pattern and convolutional neural network;the combined denoising of images on the optical and thermal range onboard the UAV;portable flow device using Fourier ptychography microscopy and deep learning for detection of biosignatures;parallel color image watermarking scheme for multiple picture object based on multithreading coding;and performance analysis of semantic segmentation algorithms trained with JPEG compressed datasets.

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Detection of Overhanging and Dead Branches from In-Vehicle Camera images Using deep learning and image processing 13

Detection of Overhanging and Dead Branches from In-Vehicle C...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Okachi, Tomoka Nomura, Yasutoshi Graduate School of Science and Engineering Ritsumeikan University Kusatsu Japan Ritsumeikan University Civil and Environmental Engineering Kusatsu Japan

ISBN: (纸本)9798350373332

In recent years, the construction industry has been promoting ICT (Information and Communication Technology) to address the decline in construction workers and improve working conditions. However, despite advancements in technology, the industry still relies on the visual inspections of skilled technicians, leading to enormous time and cost expenditures for the maintenance of social infrastructure and surrounding obstacles. In light of this situation, there is a growing demand for the development of inspection systems using deep learning. This study focuses on constructing an inspection system that targets dead branches on roadside trees in mountainous areas. In these areas, after typhoons, dead branches often get caught in roadside trees, posing a risk of falling. Therefore, it is necessary to check and remove them in advance. Since it is difficult to observe roadside trees while driving, the goal of this study is to improve the current situation by developing a system that can detect overhanging branches and dead branches caught in them. © 2024 IEEE.

关键词： Roadsides

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Design of an AMR Using image processing and deep learning for Monitoring Safety Aspects in Warehouse

Design of an AMR Using Image Processing and Deep Learning fo...

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IST-Africa Conference

作者： Pooloo, Nabeelah Aumeer, Wafiik Khoodeeram, Rajeev Univ Mascareignes Ave Concorde Roches Brunes 71203 Rose Hill Mauritius

ISBN: (数字)9781905824694

ISBN: (纸本)9781905824694

The latest spinoffs in the field of Autonomous Vehicles have paved way for a revolution in mobility and transportation;particularly in the warehousing and distribution sector. AMRs, Autonomous Mobile Robots, are being deployed to assist in warehousing activities as they present multiple advantages. In this paper, an AMR coupled with image processing and deep learning is introduced as a novel approach to solve a two-fold problem: surveillance and disinfection. deep learning will make use of real-time data collected by the AMR's camera as a smart surveillance method for abnormal event detection. YOLOv4 is used to train a custom dataset for object detection on five different classes. The latter obtained a 74.40% accuracy. The vehicle will also be used to diffuse disinfecting agents as a mean to sanitize the stores and stocks against Covid-19. Moreover, autonomous navigation of the AMR will be based on image processing techniques for path track detection.

关键词： Autonomous Mobile Robots image processing deep learning YOLOv4

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