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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11-20 订阅

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Face Recognition Attendance Framework using Quantum Edge processing 3

Face Recognition Attendance Framework using Quantum Edge Pro...

引用

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

作者： Rajarajeswari, S. Ranjitha, Hassan Srinivas Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9798331515911

Through the use of a Streamlit web application, the script is a highly intelligent tool that combines cutting-edge quantum processing principles with traditional computer vision approaches to handle a variety of face detection and identification tasks. While other libraries handle computer vision jobs and numerical calculations, such as NumPy and OpenCV, Streamlit is essential to developing the web application interface. While Streamlit WebRTC allows real-time video streaming, PIL is utilized for image processing. The application is set up to have a personalized look and a centered layout. Users may upload photographs, choose detection modes, and modify face detection settings on its header and sidebar. The program detects faces in uploaded photographs by encircling them with bounding boxes using the Haar Cascade Classifier. This may be used to build a face dataset or train a face recognition algorithm. Another important feature is real-time face identification, in which the software uses the Haar Cascade Classifier to identify faces in a video stream. Bounding boxes are used to emphasize faces, and photos containing faces are retained for later study or training. The program may utilize taken photographs to train a model for face recognition. These photos are processed, turned into grayscale, and then the LBPH (Local Binary Patterns Histograms) technique is used to train the model. The trained model is then kept for use in upcoming facial recognition assignments. Moreover, the program may be set up to track attendance in real time by identifying faces in a webcam stream. Time stamps are recorded, attendance is logged using recognized faces, and the information is saved to a CSV file. All things considered, the script provides a complete solution that combines traditional and quantum methods for real-time face identification, picture processing, and attendance monitoring. This makes it an adaptable tool for a range of computer vision and machine learning application

关键词： Photography

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Multi-Modal Recognition Via Multi-Head Attention Fusion of Text, Visual and Audio Content on video Datasets 3

Multi-Modal Recognition Via Multi-Head Attention Fusion of T...

引用

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

作者： Tang, Hongping School of Animation and Game China Academy of Art Zhejiang Hangzhou310024 China

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

In recent years, due to the diversification of digital information, multi-modal recognition that integrates text, visual, and audio content has become a core topic in the field of artificial intelligence. Considering the current models are not sufficient enough, this study analyzes the multi-modal recognition via multi-head attention fusion of text, visual and audio content on video datasets. The main innovation of the study contains: A new and efficient video data-set pre-processing strategy, a robust multi-head attention mechanism, and an efficient feature fusion model. In terms of modality selection, text, vision, and audio modalities are identified as research directions. As the first step of modeling, this study uses median filtering to pre-process the video data in order to remove noise and improve the quality of the video frames. This operation facilitates subsequent feature extraction. Subsequently, by applying a multi-head attention mechanism to each modality, the model can simultaneously focus on different features of the input data. Based on the extracted features, the complex relationships and dependencies between modalities are captured. The overall modeling of this study uses a self-supervised learning method to improve the accuracy of multi-modal alignment through spatio-temporal masking and contrastive learning. The recognition accuracy of the text, visual and audio modes of this model is 92.5% on average, and the normalized edit distance is 0.138 on average. The experimental results show the performance of the proposed model. © 2025 IEEE.

关键词： Spatio-temporal data

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A Performance Comparison of Multiple Deep Learning Methodologies in image to Text Synthesis Tasks 3

A Performance Comparison of Multiple Deep Learning Methodolo...

引用

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

作者： Martinez, Jude Joseph Lamug Winata, Edward Kevin Bina Nusantara International University Computer Science Department Faculty of Computing and Media Central Jakarta11480 Indonesia

ISBN: (纸本)9798331509675

Generating textual descriptions from visual inputs has become a critical area of research in the intersection of computer vision and natural language processing called image captioning. This study deep dives towards comparing a few popular image captioning methodologies, going from traditional encoder decoder architectures to more advanced techniques that incorporate attention mechanisms, transformers, and reinforcement learning. We evaluate the performance of these models on publicly available datasets containing logs from the online community Flickr on several dimensions including caption accuracy, diversity and computational efficiency. Captions are evaluated in terms of both quantitative metric(BLEU, ROUGE-N, METEOR, CIDEr, SPICE) and qualitative analysis(contextual relevance besides creativity) for the caption quality. The results show clear advantages and trade offs of each method, allowing for a holistic assessment of how each approach is suitable for real world problems. Thus, this work sheds important light on the trade offs between model complexity, efficiency and performance in image captioning tasks and provides a useful reference for researchers making similar decisions about model design in similar tasks. © 2025 IEEE.

关键词： Deep reinforcement learning

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Automated Dental Cavity Detection in Front Teeth using image processing 3

Automated Dental Cavity Detection in Front Teeth using Image...

引用

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

作者： Hampiholi, Amogh G. Kusanur, Vrunda Sujaya, B.L. R N S Institute of Technology Bengaluru India B N M Institute of Technology Bengaluru India

ISBN: (纸本)9798331515911

The increase in number of cases in dental caries has entailed the need for sophisticated diagnostic tools for timely detection and treatment. This work presents an innovative dental caries detection methodology using OpenCV, a well-known open-source computer vision library. By utilizing image processing techniques, this method accurately identifies and analyzes caries. The four stages involved in caries discovery involve image pre-processing, segmentation, feature extraction and classification. image preprocessing enhances quality of the image and removes noise, followed by segmentation, that identifies and isolates the specific region of interest. Next, feature extraction approaches identify potential caries regions by analyzing their morphological characteristics. In the next step, classification algorithm differentiates accurately between healthy and caries-affected areas. Experimental outcomes show the efficiency of this approach, presenting a high accuracy in caries detection. This automated system can not only assist in early diagnosis, but also supports dental professionals in providing improved patient care by reducing the time and subjectivity associated with traditional investigation techniques using own-trained haarcascades files. The novelty of this work is the development of the new, own-trained model in the OpenCV haarcascades toolbox as it does not contain any explicit model for detecting dental caries. © 2025 IEEE.

关键词： image segmentation

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Kidney Stone image analysis for Diagnosis 3

Kidney Stone Image analysis for Diagnosis

引用

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

作者： Sunitha, S.V. Unnathi, G.D. Dwivedi, Priyanshi Gowda, Ruthvik L. Chinmayi, R.C. Bnm Institute of Technology Dept. of Ece Bangalore India

ISBN: (纸本)9798331515911

Kidney stones are a common medical issue that, if not identified and treated quickly, can result in excruciating pain and other complications. The accuracy and intrusiveness of traditional diagnostic techniques like ultrasound and X-ray imaging are limited. This work suggests a non-invasive and effective method for kidney stone identification that makes use of MATLAB's capabilities in particular for image processing approaches. The technique entails obtaining medical images, usually CT or ultrasound scans, and using image processing algorithms to improve and assess the images. To raise the quality of the photos, pre-processing methods including contrast enhancement and noise reduction are used. After that, segmentation techniques are used to isolate the regions of interest, which enables the recognition of possible kidney stones in the images. The identified stones are then described using feature extraction techniques according to their size, shape, and textural characteristics. Kidney stones may now be automatically identified and categorized, thanks to this model, which facilitates kidney stone diagnosis. To evaluate the precision and effectiveness of the suggested system, a dataset of medical photographs is used to assess the suggested method. The findings are compared with standard diagnostic approaches. The outcomes demonstrate how MATLAB-based image processing methods may be used to accurately and quickly detect kidney stones, which can assist medical professionals in making better judgments and giving their patients better care. © 2025 IEEE.

关键词： Ultrasonic imaging

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A rate control algorithm based on content saliency feature for HEVC 3

A rate control algorithm based on content saliency feature f...

引用

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

作者： Kong, Chuizhi Gao, Lizhen College of Mechanical Electrical and Information Engineering Xiamen Institute of Technology Fujian Xiamen361021 China The Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province Fujian Xiamen361021 China

ISBN: (纸本)9781510688179

Rate control is an important module in video coding. A rate control algorithm with the saliency features of video content based on the high efficiency video coding (HEVC) standard is proposed in this paper. This algorithm is to extract the content saliency feature of natural video and bitrate is allocated to the region with strong salience more effectively. Firstly, the Gaussian pyramid of brightness and direction of natural content video was constructed by Gaussian sampling method, and the luminance and direction feature factors of video sequence images were calculated. The complexity significance factors of luminance and direction were obtained by combining the feature factors of different scales. The pixel level feature factor is used to guide the coding tree unit layer for target bit allocation. Then, a new rate-distortion model is constructed based on inter-frame correlation. Experimental results show that, compared with the reference algorithm, the average bitrate mismatch of the proposed algorithm is reduced to 0.031% under the condition of low delay coding, and the accuracy of the bit rate control is improved. Under the condition of the same quality, the average bitrate of the algorithm is saved by 3.55%, and the rate distortion (rd) performance of the encoded video is improved. © The Authors.

关键词： video signal processing

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HL-Net: Zero-shot image denoising algorithm based on hybrid learning 24

HL-Net: Zero-shot image denoising algorithm based on hybrid ...

引用

3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wang, Yi Luo, Xiaohui School of Computer and Software Engineering Xihua University Sichuan ChengDu China

ISBN: (纸本)9798400707308

Deep neural networks trained on large datasets have achieved good results in image denoising. However, networks trained on specific datasets often have poor generalization, which is not conducive to practical applications. In addition, most denoisers often ignore the detail differences between image texture areas and smooth areas during the denoising process, resulting in the loss of details in texture areas. In this paper, we propose a zero-shot denoising network (HL-Net) with a Texture Noise Attention module (TNA) and a hybrid learning architecture. TNA allows the network to better extract noise information from texture areas without destroying their details, while the hybrid learning architecture uses two denoising methods for image smooth areas and texture areas: content learning LC (learning non-noise information in noisy images) and noise learning LN (learning noise information in noisy images), which can better preserve the image details in complex texture areas while extracting content information from smooth areas of noisy images. Additionally, the loss function proposed for the zero-sample hybrid learning network uses residual loss, consistency loss, and mean loss, enabling the network to better perform content learning and noise learning. Experiments show that our method outperforms most zero-sample denoising methods on various types of synthetic noise datasets and real-world noise datasets. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Enhancing Kidney Stone Detection in CT scans with image processing and the DCNN-RELM Model 3

Enhancing Kidney Stone Detection in CT scans with Image Proc...

引用

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

作者： Prabakaran, Manjula Zakaria, Jesmin Kannan, S. Santhiya, P. Rajasekar, P. Rathnamma, M.V. Andhra Pradesh India NSHM Knowledge Campus West Bengal Durgapur India Dhanalakshmi Srinivasan University School of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Trichy India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Klm College of Engineering for Women Department of Cse Andhra Pradesh Kadapa India

ISBN: (纸本)9798331509675

CT of the urinary system, abdomen, and kidneys appears to improve kidney stone screening and identification. Segmentation and categorization are essential for kidney stone identification and management. This work developed a semi-automatic kidney stone detection system using geometric concepts and image processing. The main steps include model training, feature extraction, segmentation, embossing, and preprocessing. Preprocessing involves trimming the input image to remove unnecessary kidney regions to focus on critical CT scan locations. Relief embossing enhanced image details. Segmentation accurately classified veins and blood arteries to define kidney stone boundaries. CNN-RELM was our training model. Compared to CNN and ELM models, the recommended system performed better. Its 93.87% kidney stone detection and localization accuracy prove its reliability. The semi-automatic system's reliable kidney stone detection and segmentation method may improve clinical decision-making. Combining CNN-RELM modeling and enhanced image processing could lead to medical imaging applications. © 2025 IEEE.

关键词： Computerized tomography

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Robust image Steganography Using DCT, GrabCut, and Quantization 3rd

Robust Image Steganography Using DCT, GrabCut, and Quantizat...

引用

3rd international conference on Network Security and Blockchain Technology

作者： Kumar, Gatram Sravan Sethi, Kamalakanta Joshi, Piyush Indian Inst Informat Technol Sricity India

ISBN: (纸本)9789819780501;9789819780518

Steganography is the method by which we hide confidential information in digital media that includes images, audio, and video files. This is useful for sending confidential information without revealing its existence. This paper introduces a novel steganographic approach based on GrabCut segmentation, Advanced Encryption Standard (AES), Discrete Cosine Transform (DCT), and quantization. Our steganography approach addresses several limitations existing in traditional methods such as low embedding capacity, visual distortions in images, and vulnerability to attacks. The GrabCut algorithm is used to segment the image into foreground and background regions. DCT is used for efficient data hiding, whereas quantization helps to preserve the image quality. Our method also adds an extra layer of security by encrypting the secret message using AES before embedding. In comparison to the state-of-the-art approaches, our method gives a high Peak Signal-to-Noise Ratio (PSNR) and low Mean Square Error (MSE) which indicates the efficiency of our approach. In addition to these, we also tested the robustness of our approach against different attacks.

关键词： Steganography GrabCut DCT Quantization AES PSNR MSE

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Real-Time Aerial image Edge Detection with MicroWatt Power ISA on FPGA 3

Real-Time Aerial Image Edge Detection with MicroWatt Power I...

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

作者： Joshi, Prashant V. Sudharshan, K.M. Asuti, Majunath G. Poornashree, S.J. Research, Sunitha K. School of Ece Reva University Bengaluru India Bnmit Department of Ece Bengaluru India

ISBN: (纸本)9798331515911

This research investigates the implementation of real-time aerial image edge detection using the Canny edge detection algorithm with the MicroWatt Power Instruction Set Architecture (ISA)-Open Core Processor on Field Programmable Gate Arrays (FPGAs). Canny edge detection is particularly advantageous for aerial image processing due to its ability to produce precise and well-defined edges, making it ideal for applications in surveillance, agriculture, and environmental monitoring where detail preservation is critical. The proposed system utilizes the MicroWatt Power ISA-Open core processor to optimize resource utilization, achieving high throughput and low latency while maintaining energy efficiency. The work uses the Xilinx Vivado EDA tool to calculate the power dissipation of the FPGA implementation, demonstrating significant reductions in power consumption compared to traditional processing methods. The experimental results showcase the effectiveness of the Canny edge detector in producing accurate edge maps while minimizing resource usage, thus enabling real-time processing capabilities. This work contributes to the advancement of low-power embedded systems for aerial imaging, highlighting the potential of FPGAs combined with the MicroWatt Power ISA for efficient and effective image analysis. © 2025 IEEE.

关键词： Aerial photography

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