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检索条件"任意字段=Proceedings of the 3rd International Conference on Video and Image Processing"

共 12962 条记录，以下是21-30 订阅

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Design and Implementation of an Autonomous Line-Following Minidrone Using image processing and PID Control 3

Design and Implementation of an Autonomous Line-Following Mi...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Rekha, P. Hussain, Maaz Naveed, Misrah Bnm Institute of Technology Dept. of Ece Bangalore India Bnm Institute of Technology Bangalore India

ISBN: (纸本)9798331515911

This paper presents a comprehensive approach to controlling a Parrot Mambo minidrone in a simulation environment using MATLAB. The primary objective is to guide the drone along a predefined path while maintaining stability and precision. Optical flow technique and Inertial Measurement Unit sensor data are used for real-time position and velocity estimation. Path planning algorithms, coupled with control strategies such as Kalman filtering and dynamic error correction, ensure the drone follows the optimal path to reach its target. Simulation results demonstrate the effectiveness of the proposed system in successfully navigating the drone through complex maneuvers with minimal drift. © 2025 IEEE.

关键词： MATLAB

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proceedings of the 2024 6th international conference on video, Signal and image processing, VSIP 2024

Proceedings of the 2024 6th International Conference on Vide...

引用

6th international conference on video, Signal and image processing, VSIP 2024

ISBN: (纸本)9798400709647

The proceedings contain 26 papers. The topics discussed include: underwater aquaculture object contour detection dataset with benchmarks;the detection of foreign objects on coal mine conveyor belts based on semi-supervised learning;abnormal cable detection at the bottom of high-speed train using improved Yolov8;research on image edge detection algorithms based on fractional-order differentiation;weakly supervised salient object detection via hybrid pseudo labels;region feature-guided automated seed selection region growing method for chip-surface-defect detection;enhanced multi-modal heart segmentation approach based on SWIN-transformer for small datasets;blind image quality assessment based on human visual perception;and hybrid loss and 3D convolutional multilayer perceptron for hyperspectral images classification with noisy labels.

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AI-based Automated Subtitle Generation System for Multilingual video Transcription and Embedding 3

AI-based Automated Subtitle Generation System for Multilingu...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Penyameen, K. Siva Suriya Rajan, G.M. Arshath Ahamed, A. Yugesh Ram, S. John Shiny, J. Periya Nayaki, A. Department of Information Technology Velammal College of Engineering and Technology Tamilnadu Madurai India

ISBN: (纸本)9798331527549

The subtitle generator system proposed in this project is an intelligent tool towards automation of subtitle production for video content, using the most advanced speech recognition and transcription and video processing technologies. Using integration with machine learning models such as Whisper by OpenAI with multimedia tools including FFmpeg and MoviePy, it manages the production of subtitles that track up and are perfectly synchronized with spoken content within video files. It gives standard SRT file generation with direct embedding abilities into videos, hence reducing the total manual effort and time taken in the production of subtitles. Support to transcribe into multiple languages is also given with an easy Graphical User Interface GUI that provides ease of interaction with the application. This solution to video accessibility, particularly for the purpose of using more than one language, is scalable and automated to provide the best possible approach in media industry subtitle generation. © 2025 IEEE.

关键词： Graphical user interfaces

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Face Mask Detection in Real Time using Machine Learning Algorithms 3

Face Mask Detection in Real Time using Machine Learning Algo...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kavitha, R. Sultania, Tanishq Padhmashri, V. Shreya, A. Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798331509675

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet V2 and VGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet V2 yields an accuracy of 96.6% and VGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. VGG16's deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, VGG16 is highly resource intensive and needs extensive computation time. © 2025 IEEE.

关键词： image segmentation

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Deploying Emotion Recognition on Resource Constrained Device: Utilizing Light Weight Deep Learning Models with Multi-Physiological Signals 24

Deploying Emotion Recognition on Resource Constrained Device...

引用

3rd international conference on Signal processing, Computer Networks and Communications, SPCNC 2024

作者： Chen, Xu Liang, Dong Zhang, Jianwei Zhang, Yongjian Chengdu Medical College Sichuan Chengdu China University of International Relations Beijing China

ISBN: (纸本)9798400710834

In recent years, the field of emotion recognition has witnessed significant advancements, with applications spanning from healthcare monitoring to human-computer interaction. However, deploying such systems on resource-constrained end devices, such as wearable sensors or mobile phones, remains a challenging task due to limitations in computational power, memory, and battery life. This paper proposes a novel approach to address these challenges by utilizing lightweight deep learning models that are specifically designed to operate efficiently on such devices. The proposed system integrates multi-physiological signals, including Electrocardiogram (ECG), Photoplethysmographic (PPG) readings, Heartbeat Rate (HR), Skin Temperature measurements (SKT), and Respiratory patterns (RSP) to provide a comprehensive assessment of emotional states. We evaluate the performance of our system on a dataset of physiological signals collected from subjects undergoing video emotional stimuli. Experimental results demonstrate that our lightweight models achieve comparable accuracy to heavier models while significantly reducing computational overhead and power consumption. The findings of this study pave the way for the development of practical, real-time emotion recognition systems that can be deployed on a wide range of resource-constrained end devices. © 2024 Copyright held by the owner/author(s).

关键词： Physiological models

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Application of image Recognition Algorithm in Positioning and Emergency Call Function of Smart Safety Helmets 24

Application of Image Recognition Algorithm in Positioning an...

引用

3rd international conference on Signal processing, Computer Networks and Communications, SPCNC 2024

作者： Zhang, Dongdong Zhang, Kan Li, Yong Cui, Yichen Zheng, Ke Liu, Yafei Marketing Service Center of State Grid Henan Electric Power Company Henan Zhengzhou China

ISBN: (纸本)9798400710834

In terms of ensuring the safety of construction workers and cyclists, traditional positioning methods often rely on GPS signals, but the accuracy of GPS positioning is limited indoors or in signal-blocked areas. At the same time, the traditional way of asking for help may delay the rescue time due to cumbersome operation, noisy environment or psychological pressure in emergency situations. To address these problems, this paper first integrates high-definition cameras, sensors and wireless communication modules on the smart safety helmet (SSH). The captured image is then preprocessed by noise reduction, enhancement, cropping, etc., and key features such as facial features are identified through CNN. The extracted features are then matched and analyzed, and compared with pre-stored map data, landmark databases, etc. At the same time, CNN is used to identify safety signs in the image to determine whether the wearer is in a dangerous environment. The research results found that the average error of CNN-based positioning technology is 1.32 meters, which is significantly lower than the 3.56 meters of traditional GPS technology. In terms of the response time of the rescue function, the average response time of CNN-based technology is 500.5 milliseconds, and the rescue success rate is also significant. improve. Therefore, this study concludes that CNN can improve the positioning accuracy and rescue response speed of SSHs, improve rescue efficiency and safety, and show great potential in the field of safety. © 2024 Copyright held by the owner/author(s).

关键词： Noise abatement

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Research on vehicle detection algorithm based on geomagnetic adaptive threshold 3

Research on vehicle detection algorithm based on geomagnetic...

引用

3rd international conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Gao, Xiang Jia, Duowei Zhang, Haibo Xi'an University of Technology No.5 Jinhua South Road Shanxi Xi'an710048 China The 710th Research Institute of China State Shipbuilding Corporation No.58 Shengli 3rd Road Hubei Yichang443000 China

ISBN: (纸本)9781510688179

Vehicle detection is a crucial part of solving the congestion problem. Compared with the traditional video detection, geomagnetic detection has the characteristics of simple equipment, convenient signal processing and all-weather detection. A vehicle detection algorithm based on the geomagnetic adaptive threshold is proposed in this paper. Firstly, the magnetic dipole array model is introduced. According to the magnetic field characteristics of the measured vehicle, it is found that the magnetic field of the vehicle could be better simulated by using three magnetic dipoles with an interval of 2m. Then, considering the external environment interference, a detection algorithm based on the fluctuation of the magnetic field base value is studied. And the problem of magnetic shift caused by environment and temperature is solved by geomagnetic adaptive threshold. Finally, the parked state of the vehicle is judged by the state machine to prevent misjudgment. Compared with the traditional detection algorithm, the interference of the external magnetic environment can be well filtered. The detection of vehicles would be fast and accurate. © The Authors.

关键词： Magnetic levitation vehicles

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Automated Blood Group Detection System using image processing for Non- Invasive Antigen Feature Extraction and Classification 3

Automated Blood Group Detection System using Image Processin...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Reddy, Gopu Chandrasekhar SaiKrishna, Gajavada Annie Micheal, A. Department of CSE with AIML Sathyabama Institute of Science and Technology Chennai India Department of CSE Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Blood group detection is essential in medical diagnostics and transfusion medicine but often relies on invasive methods requiring specialized infrastructure. This research introduces an automated, non-invasive blood group detection system using YOLOv8 object detection and advanced image processing. The system addresses challenges such as human error, lack of accessibility in resource-limited settings, and delays in traditional methods. By analyzing high-resolution blood sample images, the system extracts antigen features and classifies agglutination patterns for A, B, and D blood groups. With a mean average precision (mAP) of 97.4% and an inference time of 15 milliseconds per image, the system delivers scalable, real-time, and robust diagnostics. This research advances healthcare accessibility and precision medicine objectives, offering an efficient and practical alternative to traditional laboratory-based methods." © 2025 IEEE.

关键词： Antigens

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Intelligent image Captioning with InceptionV3, LSTM, and PySpark Integration 3

Intelligent Image Captioning with InceptionV3, LSTM, and PyS...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kurian, Thanu Mathew, Jimsha K. Syam Dev, R.S. Anand, P.K. Vignesh, A. Rakshith, B.S. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies such as PySpark, LSTM, and InceptionV3 to develop an effective image captioning system. We harness the power of PySpark, a distributed computing framework, to efficiently process large-scale image datasets and extract high-level features from images using the InceptionV3 convolutional neural network (CNN) model. These features capture the semantic information present in the images and serve as input to the caption generation model. The caption generation model utilizes LSTM neural network as the decoder component. LSTM is well-suited for sequential data processing and is capable of generating coherent and contextually relevant captions based on the extracted image features. The InceptionV3 model acts as the encoder, extracting meaningful visual features from input images, while the LSTM decoder generates captions by decoding these features into natural language descriptions. This multimodal approach enables the model to understand and describe the content of images accurately. Through experimentation and evaluation on diverse image datasets, our proposed system demonstrates promising results in generating accurate and human-like captions for a wide range of images. By integrating PySpark, LSTM, and InceptionV3, our image captioning system achieves state-of-the-art performance, highlighting the effectiveness of leveraging advanced technologies in solving complex AI tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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Intelligent Computing System for Breast Cancer Identification utilizing U-Net for Digital image Segmentation 3

Intelligent Computing System for Breast Cancer Identificatio...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Goli, Ranadheer Reddy Jadala, Vijaya Chandra Jamalpur, Bhavana Department of Computer Science and Artificial Intelligene School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331527549

As per WHO calculation of deaths, globally due to Breast Cancer is around one million. Breast cancer can be cured by early detection, in which abnormal breast cells proliferate uncontrollably, leading to tumor formation. Early detection of breast cancer may reduce deaths, as per World Health Organization records there are about one million deaths are caused by 2024 among them only 0.5 percent are of men. The different methodologies adopted in medical image processing related to breast cancer detection methods include mammography. We concentrated on medical image segmentation which helps to get visual information about breast tissue. This paper presents an intelligent computing system designed to support medical image processing through utilizing U-Net methodology for segmentation as part of deep learning methodologies. As part of Experimental Analysis, a BUSI dataset is used for the implementation of Deep Learning methodologies. A graphic analysis based on medical images, followed by an analysis of the results. This Intelligent Computing System based on artificial intelligence helps radiologists, medical professionals, and doctors with early and simplified breast cancer diagnosis. © 2025 IEEE.

关键词： Breast Cancer Intelligent System Segmentation U-Net

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