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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

共 5158 条记录，以下是41-50 订阅

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2023 4th international conference on computer vision, image and Deep Learning, CVIDL 2023

2023 4th International Conference on Computer Vision, Image ...

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4th international conference on computer vision, image and Deep Learning, CVIDL 2023

ISBN: (纸本)9798350326444

the proceedings contain 136 papers. the topics discussed include: comparison of image generation methods based on diffusion models;research on image preprocessing and target detection of vehicle-machine cooperative system;a research on traditional tangka image classification based on visual features;research on image recognition based on neural network model learning algorithm;research on image recognition based on reinforcement learning;low-quality image binarization method based on threshold array system;comprehensive study of image processing techniques for low-altitude target recognition;visual cryptography scheme by freeform optics based on optimal mass transport;SPECT bone scan image classification by fusing multi-attention mechanism with deep residual networks;fine-grained image recognition method based on input perception joint probability prediction;deep learning in image classification: an overview;auricular images with annotations for segmenting key-organ mapping regions in auricular diagnosis;insulator image dataset generation based on generative adversarial network;structure-preserving domain adaptation network for generating pencil sketches;underwater image recovery method considering target polarization characteristics;and a dual attention network for multimodal remote sensing image matching.

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A Review on the Applications of Machine Learning and Deep Learning Algorithms for image recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. this study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. this paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. the latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. the main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. this paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Research on automatic processing and recognition of complex images combined with deep learning 4

Research on automatic processing and recognition of complex ...

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4th IEEE international conference on Data Science and computer Application, ICDSCA 2024

作者： Zhu, Mingrui Yang, Qingchen Duan, Zhizhong South China University of Technology Guangzhou China University of Electronic Science and Technology of China Chengdu China National University of Defense Technology Changsha China

ISBN: (纸本)9798350368239

With the rapid development of information technology, image processing technology plays an increasingly important role in many fields. However, traditional methods face many limitations when dealing with complex and fuzzy images, and it isn't easy to achieve accurate recognition and segmentation. Aiming at this challenge, this study developed an improved YOLOv5 complex image segmentation model with efficient complex image processing and segmentation using advanced image processing and deep learning technologies. According to the image characteristics, this study carried out efficient image preprocessing, including several steps such as grayscale processing, noise removal, edge enhancement, binarization, and morphological processing, to significantly improve the image quality and highlight key features to optimize the effective recognition area. then, this study introduced the YOLOv5 object detection model of the SE attention mechanism with the processed image as the input end. the experimental results show that the model has made remarkable achievements in key indicators such as precision and recall rate, which not only shows the strong advantages of deep learning in processing complex images but also provides strong support for further research. ©2024 IEEE.

关键词： Contrastive Learning

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. therefore, special hardware that can acquire, control, or execute in parallel is required. these specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or Graphics processing Units GPUs. this work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. this single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection Machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

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Performance Evaluation of Blob Detection Techniques Using image processing 4

Performance Evaluation of Blob Detection Techniques Using Im...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Sathva, S. Madhumathi, R. Manjula, D. MeenaKowshalya, A. Sri Ramakrishna Engineering College Department of Computer Science & Engineering Coimbatore India Department of Community Medicine Karpagam Faculty of Medical Sciences and Research Coimbatore India Government College of Technology Department of Computer Science & Engineering Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

Blob detection is a primary requirement in computer vision and image processing tasks. Unique visual traits are obtained by identifying blobs in an image. Variations in colour, texture, intensity, or shape are just examples of these attributes, which make blob detection a flexible tool for a variety of image analysis applications. the technique of separating the objects in the binary image is called Binary Large Object (blob) extraction. Every representation technique provides distinct insights into the properties of the blob, which facilitates further processing and interpretation. Techniques for classifying blobs according to their size, shape, texture, and colour are presented, highlighting their importance involving object recognition and classification. the categorization of blob detection techniques presented in this work includes, encompassing Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), Difference of Hessian (DOH), and Connected Components (Labelling). the results demonstrate that LoG and DoG detect a higher number of blobs, with LoG detecting the most (1,366 blobs), while DoH focuses on larger blobs but with a smaller count (777 blobs). Moreover, DoH had the largest mean blob size and higher variability in blob size, while DoG exhibited the fastest detection time (4.27 seconds). © 2024 IEEE.

关键词： Binary images

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Comprehensive Real-Time Intrusion Detection System Using IoT, computer vision (OpenCV), and Machine Learning (YOLO) Algorithms 4

Comprehensive Real-Time Intrusion Detection System Using IoT...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Uke, Shailaja Junghare, Pranay Kenjale, Shrishti Korade, Srushti Kothwade, Aniket Vishwakarma Institute of Technology Dept of Computer Engineering Pune India

ISBN: (纸本)9798331529635

the objective of this project is to develop an advanced quality human intrusion detection system, integrating IoT hardware with advanced software technologies. this will be done by relying on real-time video footage and using image processing techniques in the identification process of human presence and other potential threats, such as weapons. A face recognition module will be attached, which will let the entry of only authorized people and immediately detect unauthorized intrusions. the system includes live alerts for efficient monitoring and protection of secure places. this system, with high algorithms used and strong hardware, is scalable and reliable, proving to be fully comprehensive in relation to the potential threats against the security offered. © 2024 IEEE.

关键词： Network intrusion

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Facial Expression recognition Algorithm Based on Local Feature Extraction 4

Facial Expression Recognition Algorithm Based on Local Featu...

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4th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Du, Huabing Chen, Yaqi Shu, Zhong School of Electronic Information Engineering Jingchu University of Technology Hubei Jingmen China Jingmen Rongmedia Network Technology Co. Ltd Hubei Jingmen China

ISBN: (纸本)9798350359589

this paper employs the Center Symmetric Local Binary pattern (CSLBP) algorithm for extracting fine features in facial expression recognition. Additionally, the Rotation Invariant Local Phase Quantization (RILPQ) algorithm is utilized to capture accurate features for recognizing blurry facial expressions. the proposed approach achieves comprehensive and detailed extraction of different facial expression features. By constructing a multi-nonlinear SVM classifier structure model, the system accomplishes the classification of facial expression features and facilitates facial recognition. the experimental statistical data indicates that our algorithm is most suitable for a 9×9 pixel block grid. the accuracy of facial expression feature extraction is around 92%, and the accuracy of facial expression classification and recognition is also approximately 92%. For images with an 8×8 feature dimension, the algorithm's processing time is approximately 0.23 seconds. © 2024 IEEE.

关键词： Center Symmetric Local Binary pattern Facial Expression recognition image Feature Extraction Nonlinear Support Vector Machine Classifier Rotation Invariant Local Phase Quantizatio

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Face recognition for Blurry images Using Deep Learning 4

Face Recognition for Blurry Images Using Deep Learning

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4th international conference on computer Communication and Artificial Intelligence (CCAI)

作者： Nukala, Saihemanth Yuan, Xiaohong Roy, Kaushik Odeyomi, Olusola T. North Carolina A&T Univ Comp Sci Dept Greensboro NC 27411 USA

ISBN: (纸本)9798350362770;9798350362763

the rapid growth of visual surveillance and personal identification systems in smart environments has empowered the development of real-time face recognition, even under imperfect conditions. Among these challenges, recognizing faces in blurry images stands as a significant barrier to achieving consistent identification results. In this paper, we combine advanced machine learning algorithms and deep learning frameworks, integrating Generative Adversarial Networks (GANs), specifically the Generative Facial Prior (GFPGAN), to restore clarity to blurred facial images. the primary objective is to enhance the accuracy of face recognition in real-world scenarios where image quality may be compromised. In the proposed approach, the GFPGAN model serves as the primary mechanism for blind face restoration, transforming blurred images into clear and recognizable visuals. Upon achieving clarity in the image, a customized Convolutional Neural Network (CNN) is deployed to perform face recognition tasks. To showcase the efficiency of the proposed system, a comprehensive comparative analysis is carried out, comparing the results of the pretrained models VGG-Face, FaceNet, CNN, and DeepFace. Our experiments showed a high validation accuracy of 83.72% for FaceNet. Our experimental results demonstrate that the combination of GFPGAN preprocessing and deep learning models significantly improves blurry images face recognition accuracy.

关键词： face recognition computer vision deep learning deblur generative adversarial networks

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Exploration of the Fusion of FAST Algorithm and SIFT Algorithm in image Feature Extraction 4

Exploration of the Fusion of FAST Algorithm and SIFT Algorit...

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4th international conference on Electronic Information Engineering and computer Science, EIECS 2024

作者： Zuo, Jingyi Ma, Xianghua School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798331531409

In the field of computer vision, image feature extraction is the bridge connecting low-level image data with high-level visual tasks such as image matching and object recognition. the SIFT algorithm occupies an important position in feature extraction due to its outstanding stability and uniqueness, but its computational complexity is relatively high, limiting its application in scenarios with high real-time requirements, and the algorithm runs relatively slowly. In contrast, the FAST algorithm is known for its efficiency, but it does not provide a method for calculating feature descriptors and lacks invariance to scale and rotation changes, only suitable for translational image changes. therefore, this paper explores the fusion of the FAST algorithm and the SIFT algorithm, aiming to combine their strengths. © 2024 IEEE.

关键词： image matching

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A Comparative Analysis of image Captioning Techniques 4

A Comparative Analysis of Image Captioning Techniques

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4th international conference for Emerging Technology, INCET 2023

作者： Sunil, Diya theresa Safar, Seema Das, Abhijith Amijith, M.M. Joshy, Devika M. Rajagiri School of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350335750

image captioning is the task of generating a textual description that accurately represents the content of an image. this task involves combining computer vision techniques, such as object recognition and scene understanding, with natural language processing to produce a human-like description of an image. Over time, various models have been introduced to perform image captioning, all aiming to accurately describe the content of an image. these models have practical applications such as improving the accessibility of multimedia content, assisting individuals with visual impairments, medical image captioning, and enhancing image search and retrieval. this paper explores some of the models and studies their efficiency using different evaluation metrics. © 2023 IEEE.

关键词： Object recognition

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