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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461-470 订阅

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Sign Language Translation Using image processing to Audio Conversion 3

Sign Language Translation Using Image Processing to Audio Co...

引用

3rd IEEE International conference on Intelligent Techniques in Control, Optimization and Signal processing, INCOS 2024

作者： Subashini, V. Someshwaran, B. Sowmya, S. Kumar, S Ashwin Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350361186

Sign language has long been a fundamental mode of communication for deaf and mute individuals, serving as a crucial tool for inclusivity and interaction. Nonetheless, communication barriers persist as many individuals outside of these communities struggle to comprehend and utilize sign language effectively. A ground-breaking system known as hand gesture recognition using image processing to audio conversion has emerged to address this issue. This innovative system aims to develop an application capable of translating sign language and hand gestures into text and audio, thereby facilitating communication between deaf and mute individuals and the wider society. This system enables the detection and classification of hand gestures by employing computer vision techniques, such as CNN algorithms for image processing and the MediaPipe framework for hand gesture identification in real-time video streams. Subsequently, the system utilizes audio signals to provide immediate feedback by converting the detected gestures into corresponding sounds. The feature extraction CNN algorithm is implemented in Python, while the execution takes place on a Raspberry Pi connected to an external camera utilizing OpenCV libraries. Through this comprehensive approach, the system endeavours to bridge the communication gap and enhance the inclusion of deaf and mute individuals in various social settings. © 2024 IEEE.

关键词： Libraries

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A real-time Background Replacement Method Based on Machine Learning for AR Applications 48

A Real-Time Background Replacement Method Based on Machine L...

引用

48th Annual IEEE International Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Tsuboki, Yoshihiro Kawakami, Tomoya Matsumoto, Satoru Yoshihisa, Tomoki Teranishi, Yuuichi Univ Fukui Grad Sch Engn Fukui Japan Osaka Univ Cybermedia Ctr Osaka Japan Shiga Univ Fac Data Sci Hikone Shiga Japan Natl Inst Informat & Commun Technol Tokyo Japan

ISBN: (纸本)9798350376975;9798350376968

Recent technological advances in Virtual reality (VR) and Augmented reality (AR) enable users to experience a high-quality virtual world. Using VR to experience the virtual world, the user's entire view becomes the virtual world, and the user's physical movement is generally limited because the user cannot see the surrounding situation in the real world. Using AR to experience the virtual world, we generally use special sensors such as LiDAR to detect the real space and superimpose the virtual world on the real space. However, it is difficult for devices without such special sensors to detect real space and superimpose a virtual world at an appropriate position. This study proposes two methods for replacing the background: a method using depth estimation and a method using semantic segmentation. This study also confirmed that the system can be used with sufficient removal accuracy and response time by using appropriate image size for the environment and that a safe and highly immersive virtual world experience can be achieved.

关键词： augmented reality virtual reality video processing dynamic background replacement mobile computing

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Infrared small target detection method based on full-direction dilation Gaussian filtering and local background estimation

Infrared small target detection method based on full-directi...

引用

2023 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Sun, Xiyu Pan, Shengda College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9781510677685

Addressing the limitations of currently rare small target detection algorithms based on Human Visual Systems (HVS) that struggle with achieving satisfactory performance in complex backgrounds and lack high real-time capabilities, this paper introduces an innovative small target detection algorithm. This approach is founded on the integration of the Full-Direction Dilation Difference of Gaussians (FDD-DOG) and background estimation based on local contrast (LCBE). First, the high-frequency signal in the target area is enhanced by using the FDD-DOG operation, based on the difference between the real target and the background. Then, the background noise is further suppressed by assigning appropriate weights to the enhanced image by using local contrast-based background estimation. After that, the target to be detected is extracted by the adaptive threshold segmentation method. Experiments comparing the algorithm proposed in this paper with other HVS-based detection algorithms show that the method in this paper exhibits better detection performance in different infrared image sequences, where the background suppression factor is improved by an average of about 25 times and the signal-to-clutter ratio gain is improved by an average of about 15 times. © 2024 SPIE.

关键词： image enhancement

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ONNS: Experimental Evaluation of Human Smile Recognition Based on image processing Assisted Optimized Neural Network Strategy

ONNS: Experimental Evaluation of Human Smile Recognition Bas...

引用

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

作者： Murugan, K. Srilakshmi, V. Swathi, A. Neeraj, Sunheriya Afaneh, Suha Thirupathaiah, C. Bannari Amman Institute of Technology Department of Ece Sathvamangalam India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh Prakasam District523272 India Qis College of Engineering and Technology Department of Cse Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh Prakasam District523272 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa university Faculty of information technology Department of cybersecurity Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia Annamacharya University Department of Mechanical Engineering Rajampet India

ISBN: (纸本)9798331543617

This paper presents the experimental evaluation of human smile recognition using an image processing-assisted optimized neural network strategy (ONNS). The primary objective was to assess different neural network architectures and feature extraction techniques to improve the accuracy and real-time performance of smile detection. Several deep learning architectures, including a standard CNN, optimized CNN, BKNet, RetinaNet, and MobileNet, were evaluated on a smile recognition dataset. Geometric and texture-based feature extraction techniques such as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) were employed to improve the robustness of the model. The results showed that the optimized CNN achieved the highest accuracy of 95.3%, with a precision of 93.6% and a recall of 94.1%. Furthermore, a real-time performance evaluation demonstrated that MobileNet, while achieving the fastest inference time of 10 ms (100 fps), had a slightly lower accuracy of 90.5%. The combination of geometric and texture features led to improved performance, with the combined method achieving an accuracy of 92%. These results indicate that the proposed optimized neural network strategy is effective in improving smile recognition accuracy while maintaining real-time performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on real-time Data Transmission and Signal processing System of CIM Practical Training Teaching Platform Based on 5G Network 2

Research on Real-Time Data Transmission and Signal Processin...

引用

2nd IEEE International conference on image processing and Computer Applications, ICIPCA 2024

作者： Cheng, Xuemei Zhang, Hao Shandong University of Engineering and Vocational Technology Jinan250000 China Poly Shandong Real Estate Group Corporation Limited Jinan250000 China

ISBN: (纸本)9798350360240

In order to solve the problems of 'digital signal processing', such as many common knowledge points, difficult teaching, difficult visual teaching, and so on, a software platform for 'signal processing' open teaching is built based on Visual C++6.0. This system is to cooperate with the teaching content of CIM, but also to improve the practical ability of students, so that they can more easily and quickly understand some of the more complex digital signal theory. The timing of the beam position signal is optimized based on the existing down sampling method, and the digital AGC and slow application signal processing modules are completed. We need to get turn-by-turn, quick application, and track closing data simultaneously. The experiment shows that the system can obtain the multi-frequency position information including the actual running state well. The advantages of this system are easy to control parameters, easy to use, and strong expansibility. This project plans to further improve the system in the future research work so as to achieve better teaching and practical application purposes. © 2024 IEEE.

关键词： 5G mobile communication systems

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Brightening the shadows: low-light image feature enhancement

Brightening the shadows: low-light image feature enhancement

引用

conference on Multimodal image Exploitation and Learning

作者： Halford, Gavin Depoian, Arthur C., II Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673854;9781510673847

Low-light image enhancement plays a crucial role for applications in security, photography, medical imaging, and scientific research. Traditional enhancement methods, including multi-spectral hardware and contrast adjustments via computer vision, often fall short due to current hardware limitations or the sparse data available in low-light conditions. This paper introduces an innovative approach that significantly improves the brightness and overall quality of low-light images, focusing on enhanced feature extraction. Our method efficiently and accurately compensates for missing data in real-time, making it highly suitable for scenarios that demand immediate processing. This is particularly beneficial for surveillance applications, where the clarity of images is essential for swift decision-making.

关键词： LOL Dataset Low-Light Enhancement Efficient AI

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Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure image Enhancement 27th

Unified-EGformer: Exposure Guided Lightweight Transformer fo...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Adhikarla, Eashan Zhang, Kai VidalMata, Rosaura G. Aithal, Manjushree Madhusudhana, Nikhil Ambha Nicholson, John Sun, Lichao Davison, Brian D. Lehigh University BethlehemPA18015 United States Lenovo Research Morrisville United States

ISBN: (纸本)9783031781094

Despite recent strides made by AI in image processing, the issue of mixed exposure, pivotal in many real-world scenarios like surveillance and photography, remains a challenge. Traditional image enhancement techniques and current transformer models are limited with primary focus on either overexposure or underexposure. To bridge this gap, we introduce the Unified-Exposure Guided Transformer (Unified-EGformer). Our proposed solution is built upon advanced transformer architectures, equipped with local pixel-level refinement and global refinement blocks for color correction and image-wide adjustments. We employ a guided attention mechanism to precisely identify exposure-compromised regions, ensuring its adaptability across various real-world conditions. U-EGformer, with a lightweight design featuring a memory footprint (peak memory) of only ∼1134 MB (0.1 Million parameters) and an inference time of 95 ms (over 9x faster than typical existing implementations, is a viable choice for real-time applications such as surveillance and autonomous navigation. Additionally, our model is highly generalizable, requiring minimal fine-tuning to handle multiple tasks and datasets with a single architecture. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image enhancement

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Fabric Defect Detection System Using image processing 1

Fabric Defect Detection System Using Image Processing

引用

1st International conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Kulkarni, Mukund Cholke, Puja Chavan Vasmatkar, Parimal Tambe, Shravani Anuse, Samarth Telgote, Siddhant Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350361780

This study introduces a Textile Analysis and Quality Control System in response to the urgent need for innovation in the textile sector. The main objective is to tackle the shortcomings of the existing quality control techniques, which are mostly reactive and do not have the ability to monitor in real-time. Key goals of the research include developing sophisticated image processing algorithms for autonomous defect recognition, improving the accuracy and efficiency of defect classification, and incorporating advanced statistical analysis and machine learning techniques to assess textile material quality comprehensively. All of these efforts will contribute to an overall improvement in the accuracy and efficiency of quality control procedures. By creating an intuitive interface that maximizes administrator interactions, the authors make a substantial contribution. This interface makes it easier to input photos, evaluate findings quickly, and change parameters dynamically. In contrast to conventional reactive approaches, the system's proactive approach to quality control, enabled by real-time monitoring, emphasizes the significance of prompt remedial action for continuous quality control during production. The authors' methodology involves methodical steps such as thorough analysis, confirmation, and iterative improvements to demonstrate the system's efficacy and flexibility in a variety of production situations. The strong theoretical basis of the system is strengthened by this repeated process. The research has significant practical ramifications, establishing the suggested Textile Analysis and Quality Control System as an essential instrument for raising accuracy, productivity, and general industry standards for quality. The study creates the foundation for a future in which textile production embraces increased productivity and precision by fusing cutting-edge image processing techniques with time-honored approaches for revolutionary effects. © 2024 IEEE.

关键词： Quality control

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Enhancing Prosthetic Control with Ultrasound images: A Convolutional Neural Network Approach for Hand Gesture Recognition 4

Enhancing Prosthetic Control with Ultrasound Images: A Convo...

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4th Modeling, Estimation, and Control conference (MECC)

作者： Chen, Yun Bao, Xuefeng He, Hongsheng Zhang, Qiang Univ Alabama Dept Mech Engn Tuscaloosa AL 35487 USA Univ Wisconsin Dept Biomed Engn Milwaukee WI 53045 USA Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA

Human hand gesture recognition using biological signals from the forearm is an increasingly significant area of research, with implications across various fields such as prosthetic development, rehabilitation, and human-machine interaction. However, traditional hand gesture recognition with surface electromyography (sEMG) technique has some challenges, including cross-talk from neighboring muscles, low signal-to-noise ratio, and inability to measure deep muscles. In the current study, we proposed to use brightness mode (B-mode) ultrasound images from the muscles of the forearm anterior side as an alternative neuromuscular interface to recognize hand gestures. We designed a convolutional neural network (CNN) classifier to build the personalized mapping from static ultrasound images to eight different hand gestures. To evaluate the performance of the proposed CNN classifier, an ultrasound images dataset and labeled gestures from four young healthy participants were collected and analyzed. Results from offline intra-subject (personalization) validation, quasi-real-time validation, and real-time validation showed high classification accuracy of 99.65%, 97.47%, and 90.83%, respectively. In addition, real-time hand gesture recognition could be executed within 50 ms per image frame by using the proposed CNN classifier. Our findings demonstrated promising real-time hand gesture recognition with high accuracy by using B-mode ultrasound images and the proposed CNN classifier for prosthetic hand control. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Biomedical and medical image processing and systems Rehabilitation engineering and healthcare delivery Bio-signals analysis and interpretation Man-machine interfaces Bioinformatics Estimation and control in biological systems

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Research on real-time image Stitching for Wafer Defect Inspection

Research on Real-time Image Stitching for Wafer Defect Inspe...

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International conference on Computational Science and Computational Intelligence (CSCI)

作者： Kwon, So -Young Kim, Young -Hying Jung, Dong-Soo Lee, Yong-Hwan Kumoh Natl Inst Technol Dept Elect Engn Gumi South Korea Kumoh Natl Inst Technol Dept IT Convergence Gumi South Korea Syisolution Cheongju South Korea Kumoh Natl Inst Technol Sch Elect Engn Gumi South Korea

ISBN: (纸本)9798350361513;9798350372304

Recently, with the development of the 4th industrial revolution technology, the importance and demand for memory semiconductors have increased rapidly, and the semiconductor market is expected to continue to expand in the future. One of the important processes in the semiconductor industry is wafer production, and since wafer yield is directly related to a company's competitiveness, improving wafer yield is a very important issue. Therefore, it is important to accurately detect and analyze wafer defects during semiconductor production. A predictive system is used to determine the cause of defects, in which vision camera and scope equipment is used. The prediction system currently in use perform manually when combining multiple photographic images into one image, which increases the time required to analyze defects and has the potential to result in production disruption. In this paper, we discuss an algorithm that automatically combining multiple photographic images and a method of implementing it with FPGA for real-time processing.

关键词： wafer image stitching defect inspection FPGA vision system

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