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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

共 6052 条记录，以下是91-100 订阅

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25th International conference on Parallel and Distributed Computing, applications and Technologies, PDCAT 2024

25th International Conference on Parallel and Distributed Co...

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25th International conference on Parallel and Distributed Computing, applications and Technologies, PDCAT 2024

ISBN: (纸本)9789819642069

The proceedings contain 55 papers. The special focus in this conference is on Parallel and Distributed Computing: applications and Technologies. The topics include: A Meta-reinforcement learning Framework for Adaptive Quadrotor UAV Attitude Control;Securing Energy Transactions for Electric Vehicles: The Blockchain Approach and Encrypted NFTs;optimizing Task Allocation in Heterogeneous Agent Manufacturing Systems;MPG: Multi-modal Personal Health Graph for Alzheimer’s Disease Diagnosis;SMAC: A Secure Multi-authority Access Control Scheme with Attribute Unification for Fog Enabled IoT in E-Health;Convolutional neural networks Parameter Training for SCM Algorithm Based on Hausdorff Difference;handling Non-stationarity with Distribution Shifts and Data Dependency in Time Series Forecasting;the Two-Stage Stochastic Facility Location Game;regularized Non-monotone γ-weakly Submodular Maximization;fed-MoE: Efficient Federated learning for Mixture-of-Experts Models via Empirical Pruning;WaitIO-Hybrid: Communication for Coupling MPI Programs Among Heterogeneous Systems;the Material Delivery Route Prediction Method Based on Deep Reinforcement learning;privacy-Preserving in Medical image Analysis: A Review of Methods and applications;research on Task Migration Problem Based on Link Uncertainty in Adversarial Scenarios;optimizing Production Component Scheduling in Multivariate Industrial networks with Dynamic Changes in Production Costs;multi-agent Collaboration for Time-Sensitive Tasks in Multiple Networked Adversarial Scenarios;containerized Data-Flow processing for Scalable Real-Time Analytics on Edge Devices;fast Approximation for Scheduling Malleable Jobs on Parallel Batch machines with Rejection;real-Time and In-Situ Temperature Profiling for Determining Detonation of White Dwarf Mergers;accparser: A Standalone OpenACC Parser and Its Usage on Mapping OpenACC to OpenMP Directives;Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams;long-Term and Periodicity-Aware Spatio

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Realistic and Visually-Pleasing 3D Generation of Indoor Scenes from a Single image 7th

Realistic and Visually-Pleasing 3D Generation of Indoor Scen...

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7th Chinese conference on Pattern Recognition and Computer Vision

作者： Li, Jie Wang, Lei Chen, Gongbin Li, Ang Qiu, Yuhao Wu, Jiaji Cheng, Jun Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci CAS Beijing Peoples R China Shenzhen MSU BIT Univ Shenzhen Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9789819785070;9789819785087

Artificial Intelligence Generated Content (AIGC) has experienced significant advancements, particularly in the areas of natural language processing and 2D image generation. However, the generation of three-dimensional (3D) content from a single image still poses challenges, particularly when the input image contains complex backgrounds. This limitation hinders the potential applications of AIGC in areas such as human-machine interaction, virtual reality (VR), and architectural design. Despite the progress made so far, existing methods face difficulties when dealing with single images that have intricate backgrounds. Their reconstructed 3D shapes tend to be incomplete, noisy, or lack of partial geometric structures. In this paper, we introduce a 3D generation framework for indoor scenes from a single image to generate realistic and visually-pleasing 3D geometry shapes, without the requirement of point clouds, multi-view images, depth or masks as input. The main idea of our method is clustering-based 3D shape learning and prediction, followed by a shape deformation. Since more than one objects tend to be existing in indoor scenes, our framework will simultaneously generate multi-objects and predict the layout with a camera pose, as well as 3D object bounding boxes for holistic 3D scene understanding. We have evaluated the proposed framework on benchmark datasets including ShapeNet, SUN RGB-D and Pix3D, and state-of-the-art performance has been achieved. We have also given examples to illustrate immediate applications in virtual reality.

关键词： 3D mesh Reconstruction Deep neural networks

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and applications, CIAPP 2024, held in conjunction with Mexican International conference on Artificial Intelligence, MICAI 2024

17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th ...

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and applications, CIAPP 2024, held in conjunction with Mexican International conference on Artificial Intelligence, MICAI 2024

ISBN: (纸本)9783031838781

The proceedings contain 38 papers. The special focus in this conference is on Hybrid Intelligent Systems. The topics include: Integration of Alexa and Social IoT for Generation X: A Study on the Optimization Use of Smart Treadmill to Improve Physical Health;logarithmic Weighted Random Selector Algorithm: A Novel Approach for Biasing Selection Based on Positional Order Without Hyperparameters;design and Implementation of a machine learning Model for Soccer Match Prediction Based on Player Statistics;electric Scooters and Renewable Energy Integration Associated with Tourist Parks: A Dijkstra-Based Model for Smart Mobility Optimization;use of Convolutional neural networks for the Recognition of Bird Species in Risk Categories in the State of Chihuahua;exploring Deep learning applications in Neurodegenerative Diseases: A State-of-the-Art Review;Automated Insights: LLMs in Neurodegenerative Disease Research and Comparison;convolutional neural Network Models for Classifying of Peach (Prunus persica L);Cleaning Binary Distortion on MNIST Dataset;rule-Based Expert System with Bayesian Theory and Fuzzy Inference for Vocational Guidance: A Tool to Prevent School Dropouts;detection of Basic Motorcycle Faults Using a Fuzzy Bayesian Expert System;intervention Model with Data Mining Techniques to Work with Dating Violence Victims Using a Social Support Network;enhanced Pest Detection Using Quaternion-Based image Segmentation in Yellow Sticky Trap Samples for Precision Agriculture;Optimization of a Treatment Plant Through the Incorporation of New Waste Separation Components: A TOPSIS-Focused Multi-criteria Analysis Approach;multicriteria Analysis Applied to the Selection of Shopping Centers for a Family-Owned Restaurant Business in Smart Cities: A Case Study in Ciudad Juárez.

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A Convolutional Recurrent Mixer Network For Radar Meteorological image Super-Resolution

A Convolutional Recurrent Mixer Network For Radar Meteorolog...

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

作者： Pires, Rafael Gonçalves Santos, Daniel F.S. Calheiros, Roberto V. Papa, João Paulo Lee, Ik Hyun Bakshi, Sambit Muhammad, Khan Department of Computing São Paulo State University Bauru Brazil Meteorological Research Institute São Paulo State University Bauru Brazil Dept. of Mechatronics Engineering Tech University of Korea IKLAB Inc. Korea Republic of Dept. of CS&E National Institute of Technology Rourkela India School of Convergence Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9798350368741

image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counterparts, often affected by sensor limitations or environmental factors. Convolutional neural networks (CNNs) are state-of-the-art for SR tasks but computationally heavy. This paper introduces a novel CRMN (Convolutional Recurrent Mixer Network), a hybrid deep learning-based SR technique designed to address the complexity of CNNs, which is validated in the context of meteorological radar images. Experiments on public benchmark datasets (Berkley432 and T291) and our newly manually collected precipitation dataset from the Meteorological Research Institute (IPMET) show that our CRMN model provides competitive results compared to leading SR methods with significantly fewer parameters, making it a promising and practical solution for SR applications, particularly radar meteorology. © 2025 IEEE.

关键词： Convolutional neural networks Deep learning Meteorology Recurrent Mixer Network Super Resolution

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th ...

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ISBN: (纸本)9783031838811

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Character Recognition Using Convolutional neural networks Based on CUDA

Character Recognition Using Convolutional Neural Networks Ba...

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International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Set Paing Tun Applied Mathematics and Artificial Intelligence Department National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The article explores character recognition using convolutional neural networks (CNNs) optimized with the CUDA platform to enhance computational efficiency. It outlines the CNN architecture, methods for leveraging GPU-based parallel data processing, and presents experimental results derived from the MNIST dataset. The study highlights that implementing CUDA drastically reduces processing time while maintaining a high level of predictive accuracy. The findings emphasize the potential of GPU acceleration in handling intensive computational tasks, making it a promising approach for real-time applications in image recognition and machine learning.

关键词： Deep learning Training Energy consumption Graphics processing units Parallel processing Real-time systems Energy efficiency Convolutional neural networks Character recognition Optimization

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Language Prediction of Twitch Streamers using Graph Convolutional Network

Language Prediction of Twitch Streamers using Graph Convolut...

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International image processing, applications and Systems conference (IPAS)

作者： Md. Nahid Hasan Nakshi Saha Md. Anisur Rahman Dept. of CSE Dhaka International University Dhaka Bangladesh CSE Discipline Khulna University Khulna Bangladesh

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Social Network is a common example of the graph structure. There are many algorithms available for predicting different elements of social networks. Language is one of the elements. Prediction problems generally stay within 2D information. Graphs provide more structural information like edge relationship edge feature etc. While predicting social network elements, information about graph structure can be very useful. Graph Convolutional Network gives the opportunity to use graph structure as a piece of new information. Twitch is a popular social media platform where people stream gaming-related content. In our work, we have proposed a Graph Convolutional Network model for predicting the language of Twitch gamers. We also showed that it outperforms other machine learning and deep learning algorithms like Decision trees, Random Forest, Support Vector machine, neural Network and others. While other models could only reach 45.8% accuracy, GCN was able to achieve 73.43% by utilizing the graph structure.

关键词： Deep learning Support vector machines machine learning algorithms Social networking (online) Graph convolutional networks neural networks Streaming media Predictive models Prediction algorithms Random forests

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Proceedings of SPIE-IS&T Electronic Imaging - applications of neural networks and machine learning in image processing ix

Proceedings of SPIE-IS&T Electronic Imaging - Applications o...

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Proceedings of SPIE-IS and T Electronic Imaging - applications of neural networks and machine learning in image processing ix

The proceedings contain 17 papers from the Proceedings of SPIE-IS&T Electronic Imaging - applications of neural networks and machine learning in image processing ix. The topics discussed include: content-adaptive neural filters for image interpolation using pixel classification;classification of a set of vectors using self-organizing map- and rule-based technique;content-based document enhancement by fuzzy clustering with spatial constraints;independent component analysis for hyperspectral imagery plant classification;possibilistic particle swarms for optimization;removal of spatially correlated noise by independent component analysis;and tactical image parameter adjustment for stereo pair correlation.

关键词： Imaging techniques

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Convolutional neural Network Models for Classifying of Peach (Prunus persica L) 17th

Convolutional Neural Network Models for Classifying of Peach...

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作者： Puma-Ttito, Flossi Guerrero-Mendez, Carlos Lopez-Betancur, Daniela Saucedo-Anaya, Tonatiuh Castaneda-Diaz, Rafael Martinez-Ytuza, Luis Zacatecas Mexico Zacatecas Mexico

ISBN: (纸本)9783031838781

In this research, a group of convolutional neural network (CNN) architectures for peach image classification task were compared. The models were trained to identify the categories of a set of images that could be found in a sorting machine, such as "Healthy" or "Damaged", which correspond to the separation of peaches with or without damage. Specifically, five ResNet models (ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152) from the Torchvision library were evaluated for their ability to categorize peach images. The models were trained employing feature extraction in a transfer learning approach and evaluated using established metrics. The evaluation results indicate that ResNet-18 is the optimal CNN model for this task. The metrics for ResNet-18 are as follows: Accuracy – 95.68%, Precision – 95.74%, Recall – 95.68%, Specificity – 90.81%, F1-score – 95.61%, Geometric Mean – 93.07%, and Index of Balanced Accuracy – 87.04%. By using the AI-based grading system, peach producers could optimize their classification production and have each product evaluated according to international standards. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Text Recognition in Persian Documents Using Self-Supervised learning

Text Recognition in Persian Documents Using Self-Supervised ...

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International image processing, applications and Systems conference (IPAS)

作者： Atefeh Babaei Alireza Dehghani Mehran Yazdi Ali Jamshidi Mahdi Rasouli School of Electrical and Computer Engineering Shiraz University Shiraz Iran CeADAR Ireland's Centre For AI Dublin Ireland School of Computer Science and Mathematics Université Grenoble Alpes Grenoble France

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Text is one of humankind's most significant inventions essential for communication and collaboration in modern society. Extracting text from images, especially for languages with cursive and connected scripts like Persian, presents unique challenges due to issues such as font types, word structures, and the scarcity of labeled data. This paper addresses these challenges by implementing deep learning techniques, specifically self-supervised learning with convolutional and recurrent neural networks (CNNs and RNNs). Our approach successfully mitigates the data scarcity problem, achieving an accuracy of 92.9% on the prepared dataset.

关键词： Deep learning Technological innovation Recurrent neural networks Accuracy Text recognition image processing Collaboration Self-supervised learning Data mining Convolutional neural networks

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