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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15616 条记录，以下是431-440 订阅

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Application-Specific image and video processing Techniques for Security and Surveillance 15

Application-Specific Image and Video Processing Techniques f...

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kalanandhini, G. Sidhu, Amritpal Malathi, H. Dedgaonkar, Suruchi G. Singh, K. Ranjith Rampal, Sourav Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Karnataka Bangalore India Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore641021 India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India

ISBN: (纸本)9798350370249

Utility-particular photo and video processing strategies for protection and surveillance are a set of algorithms and techniques used to procedure and examine pictures and motion pictures captured by protection and surveillance systems. These techniques are used to discover and understand items and identify suspicious activities and music-shifting objects in actual time. Common strategies include motion detection, item recognition, facial recognition, item monitoring, and anomaly detection. Those strategies are used to enhance the accuracy and efficiency of security and surveillance structures and to provide extra dependable and relaxed monitoring. ©2024 IEEE.

关键词： video signal processing

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Research on deep learning-based channel estimation algorithm in 5G high-speed mobile scenario

Research on deep learning-based channel estimation algorithm...

引用

2024 International conference on Signal processing and Communication Security, ICSPCS 2024

作者： Teng, Wang Jie, Chen Honghe Vocational and Technical College Mengzi Yunnan661199 China

ISBN: (纸本)9781510681699

In the high-speed mobile environment supported by the fifth-generation mobile communication technology, higher vehicle speeds, more frequent switching and wider bandwidth make the design of high-speed mobile communication systems more challenging. High-quality wireless communication technology is needed to support future high-speed mobile scenarios to achieve low-latency and highly reliable communication, among which anti-Doppler frequency shift technology is the key. Based on this, this paper proposes a Rayleigh channel estimation algorithm based on image super-resolution (SR) network. This method models the channel response matrix obtained by the LS method as a two-dimensional image, uses the SR network and image restoration (image restoration, IR) technology for learning optimization, and obtains the channel response at the unknown symbol. The simulation results show that the proposed algorithm is significantly better than the traditional Linear Minimum Mean Square Error (LMMSE) method. Compared with other SR-based channel estimation algorithms under low Signal to Noise Ratio (SNR), it has a 3dB gain and a 5dB gain under high SNR. © 2024 SPIE.

关键词： image reconstruction

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Communication Protocol Between Embedded Computers and Fog Computing Environment for image processing 11

Communication Protocol Between Embedded Computers and Fog Co...

引用

11th Mediterranean conference on Embedded Computing (MECO) / 3rd Summer School on Cyber-Physical + systems and Internet of Things (CPS and IoT)

作者： Zhukova, Nataly Subbotin, Alexey Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg Russia St Petersburg Electrotech Univ LETI St Petersburg Russia

ISBN: (纸本)9781665468282

In this article a new data transmission protocol between embedded computers and the fog computing environment for image processing is considered. The possibilities of using fog environment for intelligent processing of video are revealed. The problems of transmission protocols are indicated. A comparison of existing video processing systems is given. The place of embedded computers in intelligent video surveillance systems and ways of upgrading these systems are considered. Data from the devices is collected using embedded computers and visualized using IoT technologies. The developed data transmission protocol allows processing images in the fog and/or cloud for intelligent video surveillance systems. It assumes packet data transmission of series of frames with additional information that are processed using machine learning algorithms and neural networks. The effectiveness of the new systems is shown. As an example, the problem of processing video coming from cameras used in the subway to determine damage of the escalator tape and steps is considered. systems for image processing that implement the proposed protocol can be used not only in the subway, but also in many other areas where Internet of things technologies are supported.

关键词： Internet of Things mobile technology communication protocol image processing embedded computers intelligent video surveillance video processing system fog computing environment

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The astronomical object recognition and its near-zero motion detection in series of images by in situ modeling 29

The astronomical object recognition and its near-zero motion...

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29th International conference on systems, Signals and image processing (IWSSIP)

作者： Khlamov, Sergii Savanevych, Vadym Tabakova, Iryna Trunova, Tetiana Kharkiv Natl Univ Radio Elect Kharkiv Kharkiv Ukraine Kharkiv Natl Univ Radio Elect Kharkiv Dept Syst Engn Kharkiv Ukraine Kharkiv Natl Univ Radio Elect Dept Media Syst & technol Kharkiv Ukraine

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495783

In this work we have presented the approach for the astronomical object recognition and detection of its near-zero motion in the series of images and compressed videos from the "live" (online) data stream using the developed method of in situ modeling. We have described all required preconditions and constants for the method of in situ modeling. The substitutional method implemented on a maximum likelihood criterion and the method implemented on the Fisher f-criterion were selected as the detection algorithms for the near-zero motion of objects for the current research. We have analyzed the several quality indicators of detection of the objects near-zero motion by applying the statistical imitation modeling techniques and using the conditional probability of the true detection.

关键词： in situ modeling OLS-evaluation F-test maximum likelihood criterion hypothesis image recognition motion detection series of images machine vision

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Smart Healthcare: A Logical Development of Medical image processing Based Sleep Apnea Disease Detection Using Elevated Neural Classification Principle

Smart Healthcare: A Logical Development of Medical Image Pro...

引用

2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Jagadeesh, P. Radhamani, V. Ravi, T. Charulatha, G. Shahare, D.Y. Aljawawdeh, Hamzeh J. Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering college Department of Electronics and Communication Engineering Avadi Chennai India Rajalakshmi Engineering College Department of Ece Chennai India Sathyabama Institute of Science and Technology Department of Ece Chennai India Selvam College of Technology Department of Biomedical Engineering Namakkal India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of Software Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Entirely common sleep disorder is the dangerous wakefulness (Apnea) of breathing during snoring. In this study, we explore the possibility of how medical image processing can be useful and accurate method for sleep disordered breathing detection. The new approach named as Sleep Apnea Analyzing Strategy (SAAS) is firstly tested with the help of existing method namely image base Sleep Apnea Analyzer (ISAA) that use less number or data to be processed and hence running on portable device delivering results much faster than other available methods, both being cross-validated against each other. Sleep Apnea image-based Detector using Ephysiology - A new Algorithm (SAAS) We rely on sophisticated image processing techniques to extract relevant characteristics from medical images (such as x-rays, CT scans or MRIs) which are indicative of patterns typically associated with sleep apnea. The proposed methodology includes image preprocessing to improve the quality of images, feature extraction for quantification and characterization of all relevant information during OSA trials in GUI interface through well-known classification algorithms to classify patients with at least 20 de-saturations without sleep apnea. Our research aims to help in the creation of automatic diagnostic methods that will lead to early detection and treatment for sleep apnea. Our objective with this work is to provide a fair evaluation of the proposed technique in terms of sensitivity, specificity and accuracy through using a large dataset. © 2024 IEEE.

关键词： Sleep research

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Algorithm for finding reference brightness correction coefficients 3

Algorithm for finding reference brightness correction coeffi...

引用

3rd International conference on Optics, Computer Applications, and Materials Science, CMSD 2023

作者： Abdullayeva, Ozoda Beknazarova, Saida Namangan Engeneering-Construction Institute Namangan Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781510674486

This article presents an algorithm for determining reference brightness correction coefficients to improve image quality. The algorithm utilizes a combination of statistical analysis and image processing techniques to identify and correct brightness discrepancies in digital images. By establishing a robust and efficient method for calculating these coefficients, the algorithm aims to enhance the overall visual fidelity of images across various applications, such as photography, medical imaging, and remote sensing. The proposed algorithm demonstrates its effectiveness through experimental results and comparisons with existing methods, highlighting its potential for practical implementation in image processing workflows. In the world, the filtering of digital images by the convolution method with a pulse characteristic in the spectral region scientific research is being conducted to improve the quality level of digital television images, methods for modeling filtration processes and highly efficient control systems in a number of priority areas, including: on the formation of mathematical models of filtration processes, improving the methods of wavelet, Fourier, Haar, Walsh-Hadamard, Karhunen-Loev in increasing the clarity and brightness of images based on linear and nonlinear differential equations;creation of methods for eliminating additive, pulsed and adaptive-Gaussian types of noise in images using additive and adaptive filtering;methods of algorithms and software for introducing intra-frame and interframe image transformations;methods of adaptive brightness system control using the Chebyshev matrix series;methods of gradient, static and Laplace methods for image segmentation and dividing it into contours;formation of criteria and conditions for evaluating image quality. Conducting scientific research in the above research areas confirms the relevance of the topic of this article. © 2024 SPIE. All rights reserved.

关键词： image enhancement

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Multi-scale Prototypical Transformer forWhole Slide image Classification 26th

Multi-scale Prototypical Transformer forWhole Slide Image Cl...

引用

26th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Ding, Saisai Wang, Jun Li, Juncheng Shi, Jun Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9783031439865;9783031439872

Whole slide image (WSI) classification is an essential task in computational pathology. Despite the recent advances in multiple instance learning (MIL) for WSI classification, accurate classification of WSIs remains challenging due to the extreme imbalance between the positive and negative instances in bags, and the complicated pre-processing to fuse multi-scale information ofWSI. To this end, we propose a novel multi-scale prototypical Transformer (MSPT) for WSI classification, which includes a prototypical Transformer (PT) module and a multi-scale feature fusion module (MFFM). The PT is developed to reduce redundant instances in bags by integrating prototypical learning into the Transformer architecture. It substitutes all instances with cluster prototypes, which are then re-calibrated through the self-attention mechanism of Transformer. Thereafter, an MFFM is proposed to fuse the clustered prototypes of different scales, which employs MLP-Mixer to enhance the information communication between prototypes. The experimental results on two public WSI datasets demonstrate that the pro-posed MSPT outperforms all the compared algorithms, suggesting its potential applications.

关键词： Whole slide image Multiple instance learning Multi-scale feature Prototypical Transformer

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High-definition event frame generation using SoC FPGA devices 26

High-definition event frame generation using SoC FPGA device...

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26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Blachut, Krzysztof Kryjak, Tomasz AGH University of Krakow Embedded Vision Systems Group Department of Automatic Control and Robotics Krakow Poland

ISBN: (纸本)9798350304985

In this paper we have addressed the implementation of the accumulation and projection of high-resolution event data stream (HD - 1280×720 pixels) onto the image plane in FPGA devices. The results confirm the feasibility of this approach, but there are a number of challenges, limitations and trade-offs to be considered. The required hardware resources of selected data representations, such as binary frame, event frame, exponentially decaying time surface and event frequency, were compared with those available on several popular platforms from AMD Xilinx. The resulting event frames can be used for typical vision algorithms, such as object classification and detection, using both classical and deep neural network methods. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： System-on-chip

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Research on Load Forecasting of Power systems Based on GA-BP Neural Network

Research on Load Forecasting of Power Systems Based on GA-BP...

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2023 IEEE International conference on image processing and Computer Applications, ICIPCA 2023

作者： Zheng, Xiaoru Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350314670

Aiming at the problem of low accuracy of short-term load forecasting in power system, this study combines genetic algorithm GA and BP neural network to establish a short-term forecasting model for power system load forecasting. Firstly, the weights and thresholds of BP neural network are optimized using genetic algorithm GA to get the optimal solution of BP neural network prediction model. Then the validity of the prediction model is verified by examples. The results show that the improved model can effectively reduce the measurement error of the BP neural network model and improve the prediction accuracy, which provides a reference and reference for the development of short-term load forecasting in China's power system. © 2023 IEEE.

关键词： Genetic algorithms

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Design and Development of DenseNet Transfer Learning Model for image Classification 4

Design and Development of DenseNet Transfer Learning Model f...

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4th International conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Attri, vikas Kapoor, Ankush Singh, Simranjeet Sharma, Payal University School of Computing Rayat Bahra University Mohali India University Institute of Computing Chandigarh University Mohali India

ISBN: (纸本)9798350387490

Integrating deep learning with computer vision technologies and analysing their application efficiency is the main emphasis of this research. Building hierarchical neural networks, a key component of deep learning, allows for end-to-end feature learning and semantic image processing, marking a significant advancement in the field. When training deep learning algorithms, the successes in computer vision give a solid foundation. Combining these two areas has produced cutting-edge computer vision systems that outperform the state-of-the-art in object detection and machine vision image classification. This research uses a combination of everyday image classification situations to examine the deep neural network models' better performance, draw attention to their shortcomings in interpretability and generalizability, and suggest ways to enhance them in the future. In general, advancements in computer vision will be propelled by the expanding use of deep learning and its efficient integration with large amounts of visual data. This trend will pave the way for intelligent machine vision systems. After briefly introducing deep learning, this paper proposed the most recent model used for deep learning picture classification that generates an accuracy of 96.3 %. Lastly, the accuracy of our deep learning model compared with other models used in the literature for the most challenging datasets, CIFAR-10, imageNet, and CIFAR-100. © 2024 IEEE.

关键词： Machine vision

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