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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16440 条记录，以下是1921-1930 订阅

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image Captioning System for Movie Subtitling Using Neural Networks and LSTM 3rd

Image Captioning System for Movie Subtitling Using Neural Ne...

引用

3rd International conference on Computing and Communication Networks, ICCCN 2023

作者： Vijay, K. Manohar, Eashaan Saiganesh, B. Sanjai, S. Deepak, S.R. Department of Computer Science and Engineering Rajalakshmi Engineering College Tamilnadu Chennai India Department of Electronics and Communication Engineering Rajalakshmi Engineering College Tamilnadu Chennai India

ISBN: (纸本)9789819708918

With the advent of the Internet, the multimedia business has experienced explosive growth and is now available to consumers worldwide. Particularly important to this growth has been the dominance of over-the-top (OTT) platforms during the COVID-19 epidemic. One important component in this growth has been the implementation of state-of-the-art Machine Learning (ML) methods. These algorithms can generate captions from video frames mechanically, increasing the platform's accessibility. However, it can be difficult to meet the needs of users speaking different languages, as there are many films being streamed in languages that may be inaccessible to consumers in other parts of the world. There is an issue with English being the most spoken language in the world. Therefore, individuals who aren't fluent in English may have trouble finding videos in their native language. By automatically creating English subtitles for any movie, regardless of the language spoken on the original audio track, machine learning plays a significant role in removing this barrier. Neural Networks, used for processing each frame of the video, and LSTM models, used for caption synthesis, are the key models employed here from the realm of machine learning. After the models have been trained, they can be incorporated into the user interface (UI) using a programming language like Python. In the user interface, the created caption can be shown alongside the uploaded image. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： User interfaces

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image Extraction for Linear Segment in 3D Space 3rd

Image Extraction for Linear Segment in 3D Space

引用

3rd International conference on Signal and Data processing, ICSDP 2023

作者： Mamatha, Elliriki Amardeepak, M. Sharma, Rohit Krishna Anand, S. Department of Mathematics GITAM University Bengaluru India Department of Mechanical Engineering CIT-NC Bengaluru India Department of Engineering Garden City University Bangaluru India Department of AI & DS Shridevi Institute of Engineering and Technology Tumkur India

ISBN: (纸本)9789819795772

In computer graphics systems, cropping applications appear to be quite simple from a human perspective as human eyes can easily visualize whether a portion of the picture is inside or outside. However, from a computational perspective, the problem increases multifold as a simple process involves a large number of comparisons and computations. With the added importance given to floating point numbers, the complexity shoots up exponentially. With this viewpoint in perspective, an analytical model has been designed. This model incorporates algorithms using integral calculations in order to find intermediate intersection points. This in turn would improve cropping objects’ speed in three-dimensional spaces. The performance of this algorithm is expected to be robust and can handle computations at a much higher speed for any size of rectangular parallelepiped cropping windows. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Three dimensional computer graphics

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5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

5th International Conference on Advances in Distributed Comp...

引用

5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

ISBN: (纸本)9789819735228

The proceedings contain 35 papers. The special focus in this conference is on Advances in Distributed Computing and Machine Learning. The topics include: Computer-Aided Bundle Branch Block Detection Using Symbolic Features of ECG Signal;block-Chain and Cloud-Based Tender Allocation System;reliability Assessment of IoT-Enabled systems Using Fault Trees and Bayesian Networks;resnet-50 Integrated with Attention Mechanism for Remote Sensing Classification;revolutionizing Data Annotation with Convergence of Deep Learning and Active Learning to Enhance Credibility on Twitter Datasets;statistical and Deep-Learning Approaches for Individual Carbon Footprint Calculation in India;intelligent Healthcare System Using Emerging Technologies: A Comprehensive Survey;Efficient Energy Management by Using SJF Scheduling in Wireless Sensor Network;cost Effective and Energy Efficient Drip Irrigation System for IoT Enabled Smart Agriculture;facial Detection and Recognition in Drone imagery Using FaceNet;valluvan: processing Name Board images to Enhance Communication for Native Tamil Speakers;Optimized VM Migration for Energy and Cost Reduction Using TSO Algorithm in Cloud Computing;A Study on the Mental Health Among Indian Population in the Post COVID-19 Pandemic Using Computational Intelligence;Cloud-Based Anomaly Detection for Broken Rail Track Using LSTM Autoencoders and Cross-modal Audio Analysis;Face Recognition Using CNN for Monitoring and Surveillance of Neurological Disorder Patients;a Novel Approach to Breast Cancer Histopathological image Classification Using Cross-colour Space Feature Fusion and Quantum–Classical Stack Ensemble Method;comparative Analysis of Deep Learning-Based Hybrid algorithms for Liver Disease Prediction;preface;performance Improvements of Covert Timing Channel Detection in the Era of Artificial Intelligence;sign Detection Using an N-Gram Language Model and MobileNet.

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Edge Computing Based Miniature Maps Using Embedded Webserver For Prediction of Malignancy 6

Edge Computing Based Miniature Maps Using Embedded Webserver...

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6th International conference on Devices, Circuits and systems, ICDCS 2022

作者： Johny, Anil Madhusoodanan, K.N. Cyriac, Sanju Cochin University of Science and Technology Department of Instrumentation Cochin682022 India Cusat Department of Instrumentation Cochin682022 India Rajagiri Hospital Department of Medical Oncology Kerala 683112 India

ISBN: (纸本)9781665480949

Cancer detection from histopathology images is based on extraction of spatial information from whole slide images(WSI) using image processing tools. Machine learning algorithms and state-of-the art convolution neural networks based prediction of malignancy requires cloud services which are often prone to network latency. We put forward a novel technique to classify histopathology images by creating a miniature probability maps to locate the presence or absence of cancer which can be used in any portable device connected to the local network. The shortcomings of loading the high resolution images in mobile devices is resolved as the extraction of patches from WSI and prediction is performed concurrently to minimize computational overhead. The miniature map indicating the presence or absence of cancer in pathology images can be viewed in mobile screen for the uploaded WSI from same device. A custom trained model is used to perform to reduce the processing time thereby faster predictions in end-devices deployed near the medical practitioner as assisting tool in disease diagnosis. © 2022 IEEE.

关键词： Performance evaluation image resolution Histopathology Computational modeling Neural networks Memory management Predictive models

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Controlling a Swarm of Unmanned Aerial Vehicles Using Full-Body k-Nearest Neighbor Based Action Classifier

Controlling a Swarm of Unmanned Aerial Vehicles Using Full-B...

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International conference on Unmanned Aircraft systems (ICUAS)

作者： Chaudhary, Akash Nascimento, Tiago Saska, Martin Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic Univ Fed Paraiba Joao Pessoa Paraiba Brazil

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405935

The intuitive control of robot swarms becomes crucial when humans are working in close proximity with the swarm in unknown environments. In such operations, it is necessary to maintain the autonomy of the swarm while giving the human operator enough means to influence the decision-making process of the robots. This paper presents a human-swarm interaction approach using full-body action recognition to control an autonomous flock of unmanned aerial vehicles. We estimate the full-body pose of the human operator and use a k-nearest neighbor algorithm to classify the action made by the humans. Finally, the swarm uses the identified action to decide its goal direction. We demonstrate the practicality of our approach with a multi-stage experimental setup to evaluate the prediction accuracy and robustness of the system.

关键词： image processing Decision making Lighting Autonomous aerial vehicles Prediction algorithms Robustness Real-time systems

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Communication Base Station Traffic Prediction Based on Nonlinear Regression-LSTM Decomposition Algorithm

Communication Base Station Traffic Prediction Based on Nonli...

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Signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Yanhe Tao >School of Marine Science and Technology Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Communication base station traffic prediction can help network service providers predict the peak hours of network traffic, so as to carry out network expansion and load balancing in advance, and keep the users getting a smooth network experience even during the peak hours of the network. In order to accurately predict traffic usage in different time periods, we first constructed a nonlinear regression model, then solved the parameters in the algorithm, and finally got the image of our predicted nonlinear function and compared it with the real value, and found that there are some small swings. Finally, we use the LSTM recurrent neural network to predict the swing term. The final flow prediction is obtained which is very close to the real situation. The model performance was verified with MAE, MAPE and RMSE metrics respectively, and the results showed that the values of the three metrics were 0.1873, 0.0978 and 0.2260 respectively.

关键词： Measurement Analytical models Base stations Recurrent neural networks Time series analysis Signal processing algorithms Telecommunication traffic Predictive models Prediction algorithms Long short term memory

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Attention Methods for image Denoising: A Survey

Attention Methods for Image Denoising: A Survey

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Cyber-Physical Social Intelligence (ICCSI), International conference on

作者： Zhuowei Yang Dept. School of Software Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

image denoising is an important branch of image processing, which is developed from traditional mechina learning methods to deep learning methods. Over the past few years, the focus on attention mechanisms has grown, owing to their substantial impact on various image processing applications. After the Transformer architecture was proposed, more attention mechanism-based research and improvements were proposed. However, there are few researches on image denoising based on different attention techniques. Hence, this paper delivers an exhaustive examination and overview of the utilization of attention mechanisms for image noise reduction over the past few years. We have presented three prevalent attention strategies: spatial attention, channel attention, and self-attention, and evaluated the efficacy of attention-driven image denoising techniques. Finally, this paper discusses the problems and challenges in current research and provides some inspiration for future research directions.

关键词： Surveys Learning systems Deep learning Attention mechanisms Noise reduction Computer architecture Transformers Data models Social intelligence image denoising

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Real-Time Dynamic image Stitching Using GNN-Based Feature Matching

Real-Time Dynamic Image Stitching Using GNN-Based Feature Ma...

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Mechatronics and Robotics Engineering (ICMRE), International conference on

作者： Young Hoon Suh Eun Ho Kim Yeong Gwang Choi JaeWook Jeon Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Especially in static situations, image stitching has been routinely used to seamlessly merge many photographs into a panorama. Using it in dynamic and real-time environments, however, presents a number of difficulties, such as managing shifting objects, changing surroundings, and processing limitations. An image stitching look at that works well in dynamic real-time situations is presented in this paper. The method makes use of GNN-based feature matching, dynamic object removal, and real-time synthesis techniques. Comparative studies suggest that, in comparison to traditional methods like SIFT and ORB, the suggested approach meets real-time processing requirements while achieving greater registration precision, lower homography RMSE, and consistent panoramic outputs. The findings imply that the algorithm might be used in fields where precision and efficiency are required in dynamic circumstances, like parking management systems, sports broadcasting, and autonomous driving.

关键词： Accuracy Smoothing methods Heuristic algorithms Surveillance Real-time systems Robustness image stitching Robots Optimization Sports

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Dual Adversarial Contrastive Learning for Underwater image Enhancement

Dual Adversarial Contrastive Learning for Underwater Image E...

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image processing and Media Computing (ICIPMC), International conference on

作者： Mengyi Yan xiaoyue Jiang Yuanbo Ren Jiayi Li Sihang Dang xiaoyi Feng Zhaoqiang xia Northwestern Polytechnical University Xi’an China

Underwater images are highly distorted, which makes high-level computer vision tasks difficult. Existing underwater image enhancement algorithms mainly focus on restoring the appearance of images. As a result, enhanced images may not be useful for high-level computer vision tasks. The lack of label images also makes most supervised learning networks impractical. In this paper, a dual adversarial contrastive learning enhancement network is proposed, which is both visually friendly and task-oriented. A circular network is proposed to achieve self-supervised learning between unpaired images. We also introduce a contrastive prior between the enhanced and degraded results in feature space to ensure the good visual appearance of the enhanced results. Furthermore, the high-level detection task is also used to constrain the enhanced results. The experiments were carried out on a popular underwater dataset, the enhanced images of the proposed method showed better visual quality and improve tracking performance as well.

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An Adaptive image Windowing Method for Real-Time Object Detection on Board

An Adaptive Image Windowing Method for Real-Time Object Dete...

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International conference on Electronics Technology (ICET)

作者： Long Ling Zhijun Lu Manli Shi Jie Wang Yuqing Li Minghe Hu China Academy of Space Technology Beijing Institute of Space Mechanics & Electricity Beijing China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

In order to meet the real-time detection and processing requirements of on-board targets in the field of remote sensing image processing, this paper carries out relevant research from the perspective of software optimization and proposes an onboard adaptive image windowing processing method based on differential evolution algorithm to solve the problem of shortage of on-board hardware computing resources. Based on the adaptive differential evolution algorithm, combined with the engineering application scenario, the algorithm innovatively designs an individual objective function to evaluate the “advantages and disadvantages“ of the algorithm evolution results. By coordinating all the target positions to be tracked in the image plane, the algorithm optimizes the number of windows, and considers the application boundary of the algorithm, which can effectively reduce the amount of data to be processed and greatly improve the timeliness of real-time detection on the board without losing the calculation accuracy.

关键词： Target tracking image processing Software algorithms Object detection Linear programming Real-time systems Hardware

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