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检索条件"任意字段=Conference on Real-Time Image Processing 2006"

共 25646 条记录，以下是41-50 订阅

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Sport Safety Enhancement: A Novel Approach with Monitoring Matrix-Based image Acquisition and Analysis 2

Sport Safety Enhancement: A Novel Approach with Monitoring M...

引用

2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Zhang, Xinyi Shandong Management University Department of Physical Education Jinan250357 China

ISBN: (纸本)9798331505745

With the development of video array imaging technology, the accuracy and stability of video image acquisition have been improved. In this situation, this study proposes novel approach with monitoring matrix-based image acquisition and analysis for sport safety enhancement. This study includes the following four major innovations 1) Developed an image acquisition system based on a monitoring matrix, which can accurately capture the facial information of athletes through array imaging mode;2) Proposed a new facial feature extraction algorithm, which realizes real-time analysis of facial images by fusing EAR and MAR features;3) Proposed an improved expression recognition algorithm, and the improved activation function optimizes the expression recognition network to accurately identify the facial expressions of athletes;4) Designed an image classification algorithm, and the 8 -pixel average mode is used for fuzzy information processing, so as to provide real-time warning information about the physical condition of athletes through the expression data. The experimental results show that the proposed method has satisfactory recognition accuracy on the Cambridge University Computer Laboratory's facial expression data set, and can accurately provide early warning of the athlete's status. © 2025 IEEE.

关键词： Sports

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Automated Immune Cell Classification using image processing Techniques 6

Automated Immune Cell Classification using Image Processing ...

引用

6th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Arva Hari Vardhan Abdurrahman, Usama Micheal, A. Annie Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

Advances in immunological research are essential for elucidating immune responses and developing targeted therapeutic approaches. This study proposes an automated method for immune cell classification leveraging machine learning and image processing techniques. A novel framework is introduced, combining high-resolution imaging with computational algorithms to accurately classify various immune cell types from biological samples. The methodology incorporated preprocessing steps, like normalization and noise reduction. Then, deep learning models, such as CNNs and SVMs, will be applied for feature extraction and classification. These models were trained on annotated datasets derived from flow cytometry and microscopy, demonstrating better accuracy and reliability. Results demonstrate an improvement in processing speed and accuracy of classification over traditional approaches, which underlines the importance of automation in immunological diagnostics. The data obtained can be used in high-throughput immunotherapy and disease diagnostic screening;it allows for the identification of immune cell populations within a short period and therefore improves research efficiency. The work provides a basis for real-time analysis and incorporation of multiscale datasets that will help to better understand immunity and its associated disorders. © 2025 IEEE.

关键词： Convolutional neural networks

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Research on the Application of Multi-Channel Display Technology in Virtual Scene image Generation 5

Research on the Application of Multi-Channel Display Technol...

引用

5th IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Liu, Yuanlin Wang, Lingling Haikou University of Economics Haikou571127 China

ISBN: (纸本)9798331533694

This study proposes an innovative algorithm based on DCNN and multi-channel image fusion, aiming to improve the quality and efficiency of virtual scene image generation. The algorithm extracts depth information and texture features in virtual scenes through DCNN, and combines multi-channel image fusion technology to optimize and fuse the information of different image channels (such as color, depth, normal, etc.), thereby significantly improving the image rendering effect. Through adaptive weight adjustment and image fusion strategy, the image detail performance is optimized, especially in scenes with complex lighting and rich textures, the rendering quality is significantly improved. Experimental results show that the image generation system based on this algorithm improves image quality by 15%, rendering speed by 25%, and computing resource consumption by 20%. When processing highly complex virtual scenes, the system can achieve real-time rendering and meet the performance requirements of VR and AR applications. This study provides a new and efficient solution for image generation in virtual reality and augmented reality, and demonstrates the great potential of combining multi-channel display technology with deep learning in improving rendering effects. © 2025 IEEE.

关键词： Virtual environments

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GaussianEnhancer: A General Rendering Enhancer for Gaussian Splatting

GaussianEnhancer: A General Rendering Enhancer for Gaussian ...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zou, Chen Ma, Qingsen Wang, Jia Lu, Ming Zhang, Shanghang He, Zhaofeng Beijing University of Posts and Telecommunications China Peking University China

ISBN: (纸本)9798350368741

Gaussian Splatting (GS) methods, including 3DGS and 2DGS, have demonstrated exceptional performance in real-time novel view synthesis (NVS), emerging as a transformative technology in the fields of explicit rendering and computer graphics. However, GS-based methods still face challenges in rendering high-quality image details. Even when using high-quality training frameworks, their outputs often exhibit severe rendering artifacts, such as noise and blurriness. A reasonable approach is to perform post-processing to restore clear details. In this paper, we propose GaussianEnhancer, a general network-agnostic post-processor that employs a degradation-driven view blending method to improve the rendering quality of GS models while preserving the original network's performance. Specifically, we design a degradation modeling method tailored to the GS-style and construct a large-scale training dataset to effectively simulate the native rendering artifacts of GS, enabling efficient training. In addition, we introduce a spatial information fusion framework, consisting of view fusion and depth modulation modules, which can blend highly correlated high-quality training images and leverage the depth information of the target image to complete the rendering details. Through our GaussianEnhancer, we are able to effectively eliminate the rendering artifacts of GS models and generate highly realistic synthetic views. © 2025 IEEE.

关键词： Degradation Gaussian Splatting image Enhancer

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Automated Detection of Roadway and Traffic Control Conditions Using On-Board image processing 17

Automated Detection of Roadway and Traffic Control Condition...

引用

17th International conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sivakumar, Ashmitha Jaysi Pragati, L. Roy, Sambhavi Vanajakshi, Lelitha Devi Indian Institute of Technology Madras Department of Civil Engineering Chennai India

ISBN: (纸本)9798331531195

Accurate detection of roadway and traffic control conditions is crucial for enhancing Advanced Driver Assistance Systems (ADAS) in dynamic environments. This study aims to leverage deep learning techniques, specifically YOLO series, for their identification in real-world scenarios. Detection of traffic signs, markings and signal along with road conditions such as potholes, and speed breakers are considered. The YOLO models perform well and demonstrates effective and real-time detection capacities. © 2025 IEEE.

关键词： Advanced driver assistance systems

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PiVisionSort: Integrating image processing and Machine Learning for Material Recognition on Conveyor Belts 2

PiVisionSort: Integrating Image Processing and Machine Learn...

引用

2nd International conference on Data, Information and Computing Science, CDICS 2024

作者： Kia, Shohreh Leiding, Benjamin Technische Universität Clausthal Institute for Software and System Engineering Clausthal-Zellerfeld Germany

ISBN: (纸本)9781643685762

This research aims to develop an advanced material detection system for conveyor belts, utilizing state-of-the-art image processing and machine learning techniques to automate the identification of various materials, thereby enhancing operational efficiency and accuracy in industrial settings. Current methods in material detection, such as traditional manual sorting and basic automated systems, often need more precision and adaptability in dynamic industrial environments. This paper identifies a gap in the practical and reliable detection of diverse materials under varying environmental conditions. The primary research questions addressed include: How can modern image processing and machine learning techniques improve material detection accuracy? What optimizations can be applied to ensure real-time processing efficiency? The proposed solution integrates a strategic camera setup with controlled lighting, robust image preprocessing algorithms (noise reduction, normalization, and resizing), and custom-designed detection algorithms using machine learning models. This system outperforms existing solutions by offering higher detection accuracy and adaptability to diverse industrial conditions. An interface is developed to display detection results and provide intuitive controls for system adjustments, ensuring practical use by industrial professionals. Rigorous testing and validation processes are implemented to enhance detection accuracy and processing speeds, with specific performance metrics established to measure efficacy. This paper provides a transformative impact on the manufacturing and processing industries by addressing environmental variability and material diversity. © 2025 The Authors.

关键词： Noise abatement

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FPGA-Based 3D EM/MPM Simulation Framework for Medical image Segmentation Applications 8

FPGA-Based 3D EM/MPM Simulation Framework for Medical Image ...

引用

8th International conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2025

作者： Karmakar, Samit Chakraborty, Arindam Banerjee, Saptarshi Mondal, Ayantika Dutta, Satavisha Nandi, Sayak Institute of Engineering & Management University of Engineering & Management Electronics & Communication Engineering Dept. Kolkata India

ISBN: (纸本)9798331520762

Medical image segmentation is a critical process in identifying disease-infected regions for diagnostic and treatment purposes. This paper introduces an FPGA-based framework for implementing the Expectation Maximization/Markov Random Field (E M/MP M) algorithm in 3D medical image segmentation. FPGA platforms offer unparalleled advantages in terms of parallel processing, speed, and energy efficiency, making them a promising choice for real-time medical image analysis. This proposed framework enables rapid segmentation of 3D images, leveraging the FPGA's on-chip memory and custom logic for efficient pixel classification and data processing. The adaptive design also supports the integration of external RAM for larger datasets. Simulated using Verilog HDL and Quartus Prime, this work demonstrates accurate segmentation results through virtual testing, thus showcasing the system's robustness for medical applications. © 2025 IEEE.

关键词： image segmentation

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real-time Detection of Wrong-Way Vehicles Using YOLO and image processing Techniques 4

Real-Time Detection of Wrong-Way Vehicles Using YOLO and Ima...

引用

4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Celin, J. Jeya A Jeswin, J. Faizaan, A. Mohamed Babu, A.P. Chandru Anisto, M. Arul Kalasalingam Academy of Research and Education Department of Information Technology Tamil Nadu Virudhunagar India

ISBN: (纸本)9798331523923

Due to delayed notice and incorrect identification, wrong-way driving is a major traffic safety concern that frequently results in accidents. We describe a computer vision and deep learning based real-time wrong-way vehicle identification system to tackle these issues. To identify cars in video footage and follow their movements within predetermined zones, the system makes use of the YOLO (You Only Look Once) algorithm. It correctly detects instances of driving in the incorrect direction by analysing direction, and it promptly issues warnings by sending out email notifications with pictures of the vehicles and sounding an alert. According to evidence from experiments, the system is a useful tool for increasing road safety because it improves the efficiency of detection, decreases error rates, and speeds up response times. © 2025 IEEE.

关键词： Deep learning

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image processing System for Providing Sound Information as an Assistive Tool for the Visually Impaired

Image Processing System for Providing Sound Information as a...

引用

2025 International conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Sari, Tilfi Jesi Wijaya, Beni Susilawati, Helfy Nurpadillah, Sifa Ari, Ginaldi Universitas Garut Faculty of Engineering Garut Indonesia

ISBN: (纸本)9798331513320

Blind individuals commonly use canes to help navigate their daily environments. However, this research introduces a more advanced solution by developing a smart stick that utilizes technology to differentiate between objects, such as people and vehicles, through an image processing system that delivers audio feedback via a headset. The system is powered by a Raspberry Pi microprocessor and equipped with an ultrasonic sensor to measure the distance between the user and surrounding objects. Once the sensor detects an object, the camera captures a real-time image, which is then processed using Single Shot Detector (SSD) and Cascade Classifier methods for object recognition. After successfully identifying the object, the system converts the visual information into audio signals relayed through the headset, with prompts such as 'person ahead' or 'car approaching.' Testing showed the system could accurately detect humans and vehicles, with average response times of 1.38 seconds and 4.244 seconds, respectively, and a margin of error in distance detection of approximately 0.281%. © 2025 IEEE.

关键词： Ultrasonic sensors

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Knowledge-Guided Prompt Learning for Deepfake Facial image Detection

Knowledge-Guided Prompt Learning for Deepfake Facial Image D...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Wang, Hao Deng, Cheng Zhao, Zhidong School of Cyberspace Security Hangzhou Dianzi University Hangzhou China School of Electronic Engineering Xidian University Xi'an China

ISBN: (纸本)9798350368741

Recent generative models demonstrate impressive performance on synthesizing photographic images, which makes humans hardly to distinguish them from pristine ones, especially on realistic-looking synthetic facial images. Previous works mostly focus on mining discriminative artifacts from vast amount of visual data. However, they usually lack the exploration of prior knowledge and rarely pay attention to the domain shift between training categories (e.g., natural and indoor objects) and testing ones (e.g., fine-grained human facial images), resulting in unsatisfactory detection performance. To address these issues, we propose a novel knowledge-guided prompt learning method for deepfake facial image detection. Specifically, we retrieve forgery-related prompts from large language models as expert knowledge to guide the optimization of learnable prompts. Besides, we elaborate test-time prompt tuning to alleviate the domain shift, achieving significant performance improvement and boosting the application in real-world scenarios. Extensive experiments on DeepFakeFaceForensics dataset show that our proposed approach notably outperforms state-of-the-art methods. © 2025 IEEE.

关键词： deepfake facial image detection domain shift knowledge-guided prompt learning test-time prompt tuning

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