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检索条件"任意字段=Conference on Real-Time Image Processing 2007"

共 25886 条记录，以下是261-270 订阅

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Unified image Mastery : An Integrated Approach to image Enhancement, Object Detection and Classification 4

Unified Image Mastery : An Integrated Approach to Image Enha...

引用

4th International conference on Multimedia processing, Communication and Information Technology, MPCIT 2024

作者： Vandana, N. Vidyasagar, K.N. Suguresh Hanamanal, Vachan Arun Pujari, Rahul R. School of Electronics and Communication Engineering REVA University India

ISBN: (纸本)9798350375466

The proposed system integrates YOLOv5 [1] for object detection, CMFNet [2] for image enhancement, and ResNet [3] for image classification into a unified framework for comprehensive image analysis. This integrated approach overcomes limitations of standalone methods, providing a seamless platform for various image analysis tasks. The system demonstrates improved performance in visual quality enhancement, real-time object detection, and accurate image classification across diverse scenarios. The proposed design allows for flexibility and future extensibility, making it adaptable to evolving requirements in computer vision applications. © 2024 IEEE.

关键词： image classification

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Construction of Three-dimensional real-time Rendering Model of Power Grid Based on image processing Algorithm

Construction of Three-dimensional Real-time Rendering Model ...

引用

2023 International conference on Internet of Things, Robotics and Distributed Computing, ICIRDC 2023

作者： Han, Xiao Shao, Xuefeng Xu, Lele Wang, Weitao Yu, Mengdi Ningbo Electric Power Design Institute Co. Ltd. Zhejiang Ningbo315000 China

ISBN: (纸本)9798350375275

image processing algorithm has important application value in 3D real-time rendering of power grid. By reasonable selection and application of image processing algorithm, the effect and performance of 3D visualization of power grid can be improved. Through the research on related issues, this paper aims to develop and evaluate a 3D real-time rendering model of power grid to meet the needs of power system in visualization, interaction and analysis. In order to achieve this goal, this paper adopts a set of comprehensive methods and constructs a 3D real-time rendering model with four modules: data input, rendering, interaction and analysis. Among them, the rendering module combines a variety of image processing algorithms to ensure high quality rendering results;The interactive module provides users with an intuitive and easy-to-operate interface. In order to verify the performance of the model, several groups of simulation experiments are further designed, and the whole process and data of the experiments are recorded in detail. The results show that this model has achieved a stable 60 FPS (Frames Per Second) on a high-performance computer, and the image quality has also been highly praised by users. In addition, the average user experience score reached 9.2 points. The model can not only provide powerful visual support for power system, but also be used as an analysis tool to help decision-making and planning. © 2023 IEEE.

关键词： Visualization

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FPGA-based Single-target Motion Detection Design 3

FPGA-based Single-target Motion Detection Design

引用

3rd International conference on Signal image processing and Communication, ICSIPC 2023

作者： Yin, Huansong Sun, Bingyu School of Electronic and Information Engineering Anhui Jianzhu University Anhui Hefei230601 China University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781510670945

For research at home and abroad, and to ensure the accuracy and real-time of the acquired images, to achieve the function of moving target detection, a digital image processing application based on FPGA - moving object detection is proposed, and the image acquisition and image processing two parts are studied, the image acquisition is OMINIVISION OV5640 image sensor, which comes with an embedded microprocessor to support image scaling and translation. By the programmable device FPGA to receive and send data to the FPGA external large-capacity SDRAM memory for storage, image processing is selected Intel Cyclone IV series FPGA chip, for the image acquisition process of a variety of random noise, through the verilog HDL hardware description language written RTL register transmission level code, to achieve image acquisition and storage, average filtering, expansion algorithm, corrosion algorithm, morphological operation closed calculation design The processed image is used for moving target detection and VGA display display. The system is mainly composed of camera acquisition module, SDRAM controller module, display driver module, image processing module, through design simulation, the results show that the system realizes real-time acquisition and processing of digital images, reaching 640*480 resolution display of 30 frames per second. © 2023 SPIE.

关键词： image acquisition

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real-time FPGA-Based image Classification with Deep Neural Networks

Real-Time FPGA-Based Image Classification with Deep Neural N...

引用

2024 International conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Maria Dominic Savio, M. Athiban, N. Deepa, T. Revanth, Moturi Varghese, Sharon SRM Institute of Science and Technology Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350354539

The explosion of visual data in diverse fields, from self-driving cars navigating dynamic environments to medical diagnostics demanding precise diagnoses, creates an unyielding demand for real-time image classification with high accuracy and low latency. Deep Neural Networks (DNNs) have become powerful tools for this task, but their ever-increasing complexity challenges traditional CPUs and even GPUs. This abstract explores how Field-Programmable Gate Arrays (FPGAs) can be harnessed as hardware accelerators to overcome these limitations. Field-Programmable Gate Arrays (FPGAs) emerge as a promising solution due to their unique architecture. Unlike CPUs and GPUs with fixed functionalities, FPGAs can be dynamically reconfigured to match the specific needs of a DNN, enabling efficient parallel processing and optimized execution. This tailored approach delivers superior performance compared to traditional processors, addressing the speed and power limitations crucial for real-time image classification tasks. This paper mainly focuses on optimizing the architecture to balance computational efficiency with accuracy. © 2024 IEEE.

关键词： Deep neural networks

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Test-time Optimization for Post-processing of Compressed Videos

Test-Time Optimization for Post-Processing of Compressed Vid...

引用

2024 Asia Pacific Signal and Information processing Association Annual Summit and conference

作者： Kim, Hongil Han, Changwoo Kim, Donghyun Lim, Sung-Chang Jung, Seung-Won Korea Univ Dept Elect Engn Seoul South Korea Elect & Telecommun Res Inst Hyper Real Metaverse Res Lab Daejeon South Korea

ISBN: (纸本)9798350367331;9798350367348

Lossy compression is widely used for video compression, but it often introduces compression artifacts that degrade the visual quality of compressed videos. Consequently, numerous deep learning-based methods have been developed to post-process compressed videos. However, previous post-processing models often encounter difficulties when there is a domain gap between the training and test datasets. Test-time optimization (TTO), a technique that finetunes the model during the test stage, has been considered an effective solution to address the domain gap problem. In this paper, we introduce a novel TTO method specialized for compression artifacts reduction. Specifically, we propose using image pairs available on the decoder-side, i.e., the images before and after the adaptive loop filtering of the versatile video coding standard, as input and target of TTO such that the post-processing model can be adapted to the characteristics of test data. Experimental results on several baseline models and test datasets demonstrate the effectiveness of the proposed method in post-processing compressed videos.

关键词： Video signal processing

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Enhancing Public Safety through real-time Weapon Detection: A Deep Learning Approach 3

Enhancing Public Safety through Real-time Weapon Detection: ...

引用

3rd International conference for Advancement in Technology, ICONAT 2024

作者： Dass, J. Maria Arockia Suresh, A. Swathi, K. Shalini, Paidimuddala Sinha, N A Yaswanth Kotholla Uday Kiran, U. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh Chittoor India

ISBN: (纸本)9798350354171

Date In response to the imperative need for mitigating criminal activities and ensuring public safety, this research proposes a novel approach leveraging deep learning techniques for real-time weapon detection. In contemporary society, criminal acts pose significant threats to both individuals and the broader community, necessitating proactive measures to counter such offenses swiftly. By harnessing advanced technologies, particularly deep learning algorithms like YOLOv7-tiny, this study intend to create a robust system capable of identifying weapons in surveillance footage captured by CCTV cameras. The project begins with the meticulous collection of datasets from reputable research sources, ensuring diverse and comprehensive samples for training the deep learning model. Subsequently, these datasets undergo rigorous pre-processing steps to optimize them for training, including resizing images to the required format. To facilitate accurate annotation and labelling of the dataset, advanced tools like Robo flow software are employed, streamlining the process and enhancing efficiency. Upon completion of the pre-processing phase, the YOLOv7-tiny algorithm is deployed for training the model on the annotated datasets. Through iterative training and validation processes, the model is fine-tuned to achieve optimal performance in weapon detection tasks. Once trained, the model is capable of swiftly and accurately identifying weapons in real-time surveillance footage. This project presents an effective solution for real-time weapon detection in public spaces, leveraging deep learning algorithms and advanced software tools. © 2024 IEEE.

关键词： image annotation

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High-Efficiency FPGA Implementation of Deep Learning Models for Colorectal Tumor image Analysis 2

High-Efficiency FPGA Implementation of Deep Learning Models ...

引用

2nd IEEE Global conference on Communications and Information Technologies, GCCIT 2024

作者： Asha, S. Chandru, R. Rohit, S. Saveetha Engineering College Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350388916

To meet the demand for real-time, energy-efficient solutions in medical diagnostics, this research offers a high-efficiency FPGA implementation of a convolutional neural network (CNN) for colorectal tumor picture processing. Loop unrolling, pipelining, and effective memory use were among the methods used in the creation of the CNN model, which had an optimized system architecture. With a lowered latency of 10.2 milliseconds and power usage of 4.8 watts, the model achieved an accuracy of 98.5% during training and testing on the TCGA Colorectal Tumor Dataset. With a throughput of 120 frames per second, the implementation from 2021 to 2023 significantly outperformed earlier ones. These findings highlight the possibility of FPGA-based deep learning models to provide real-time, high-performance analysis at a lower energy cost. According to the results, this strategy not only improves diagnostic precision but also provides a practical, scalable solution for clinical setting deployment, opening the door for a wider use of FPGA-based systems in medical image processing. © 2024 IEEE.

关键词： Convolutional neural networks

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An image to 3d Cross-Modal Approach for real-time 3D Human Posture Estimation 31

An Image to 3d Cross-Modal Approach for Real-Time 3D Human P...

引用

31st IEEE International conference on image processing Challenges and Workshops, ICIPCW 2024

作者： Agarwal, Swapna Sinha, Aniruddha Ghose, Avik Tata Consultancy Services Ltd. India

ISBN: (纸本)9798331515942

We present a smartphone RGB camera based system for automatic human 3D posture monitoring driven by our proposed Machine Learning (ML) backbone. Rather than mapping RGB image sequences directly to 3D posture, we learn an intermediate, concise feature space representation of 3D postures. To learn this feature space, we train an auto-encoder learnt on 3D posture sequences. We train a mapping from the image sequence to this concise 3D feature space. This mapping subtracts all unnecessary information present in the image domain and retains only 3D posture information. This elimination helps in dataset independence and increases robustness of our model. The mapped feature space vector is then mapped to 3D posture domain. For estimating 3D human pose, most States-Of-The-Art need either a 3D depth sensor which is expensive or need multiple cameras for stereo-vision. Our proposed cross-modal method uses monocular video from single low-end smartphone camera for estimating human 3D pose in real time ( 5 fps) with high accuracy (less than 1 cm error per joint ~location). We also show our method being effective for posture analysis in yoga and gym exercises. © 2024 IEEE.

关键词： Smartphones

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The Design of the aeronautical satellite image and video emergent transmission system based on interference mitigation for artificial strong jamming and channel multiple fading 24

The Design of the aeronautical satellite image and video eme...

引用

International conference on Algorithms, Software Engineering, and Network Security (ASENS)

作者： Yu, Shiyun Shi, Wei Pan, Yahan Natl Univ Def Technol Res Inst 63 Nanjing 210001 Jiangsu Peoples R China Nanjng Telecommun Technol Inst Nanjing 210000 Jiangsu Peoples R China

ISBN: (纸本)9798400709784

The uncertainty of time and place is the characteristics of taking place of the emergent incidents in the land or sea. The aeronautical satellite image and video emergent transmission system has the characteristics of high speed in motion and wider service coverage area in geography. Therefore, the aeronautical satellite image and video emergent transmission system has a great advantage over other communication methods in emergent real time image and video transmission and incident rescuing as well as other remote commanding. To insure the performance of the aeronautical satellite image and video emergent transmission system under the environment of artificial strong jamming and fading, we propose a new design of the aeronautical satellite image and video emergent transmission system based on interference mitigation for artificial strong jamming and channel multiple fading and give the design of such transmission system. The application of the mitigation method based on the adaptive antenna array is expected the very effective to reduce the influence of the artificial strong jamming and fading on the performance of the aeronautical satellite image and video emergent transmission system.

关键词： The security information transmission The aeronautical satellite communications Emergent image and video processing and transmission Jamming mitigations Multiple antenna Adaptive array nulling

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Technical Study on Underwater Target Detection and Recognition of Unmanned Surface Vehicles in Complex Environment 34th

Technical Study on Underwater Target Detection and Recogniti...

引用

34th International Ocean and Polar Engineering conference, ISOPE 2024

作者： Zhang, Shuai Wang, Fei Deng, Feifan Wang, Xiaochuan Chang, Liang Jiang, Zhengyi Taihu Laboratory of Deepsea Technological Sciences China Ship Scientific Research Center Wuxi China

ISBN: (纸本)9781880653784

The sonar sensor is affected by the medium environment, and its imaging is interfered with strong noise, which makes the detection and recognition ability of the target poor. Aiming at the complex underwater environment, this paper proposes an underwater target detection and recognition system. A new hardware structure is designed and constructed to improve the computing power, and an improved image processing algorithm is integrated on the basis of the hardware structure. The computational capability of the system and the detection and recognition ability of the algorithm are verified by experiments using specific targets. The test results show that the system can detect and identify underwater targets in real time at a processing speed of less than 90ms, and the overall target detection accuracy rate reaches 92.8% and the overall target recognition accuracy rate reaches 88.6%, which can complete the task of real-time, high-precision and all-round target detection and recognition of underwater specific targets in complex hydrological environment. © 2024 by the International Society of Offshore and Polar Engineers (ISOPE).

关键词： image enhancement

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