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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2648 条记录，以下是241-250 订阅

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A Low Power And High Performance Software Approach to artificial Intelligence On-Board 14

A Low Power And High Performance Software Approach to Artifi...

引用

14th IEEE Space Computing conference (SCC)

作者： Ghiglino, Pablo Harshe, Mandar Klepsydra Technol AG Zurich Switzerland

ISBN: (纸本)9798350341430

New generations of spacecrafts are required to perform tasks with an increased level of autonomy. Space exploration, Earth Observation, space robotics, etc. are all growing fields in Space that require more sensors and more computational power to perform these missions. Furthermore, new sensors in the market produce better quality data at higher rates while new processors can increase substantially the computational power. Therefore, near-future spacecrafts will be equipped with large number of sensors that will produce data at rates that has not been seen before in space, while at the same time, data processing power will be significantly increased. Use cases like guidance navigation and control applications, vision-based navigation has become increasingly important in a variety of space applications for enhancing autonomy and dependability. Future missions such as Active Debris Removal will rely on novel high-performance avionics to support image processing and artificial Intelligence algorithms with large workloads. Similar requirements come from Earth Observation applications, where data processing on-board can be critical in order to provide real-time reliable information to Earth. This new scenario of advanced Space applications and increase in data amount and processing power, has brought new challenges with it: low determinism, excessive power needs, data losses and large response latency. In this article, a novel approach to on-board artificial intelligence (AI) is presented that is based on state-of-the-art academic research of the well known technique of data pipeline. Algorithm pipelining has seen a resurgence in the high performance computing work due its low power use and high throughput capabilities. The approach presented here provides a very sophisticated threading model combination of pipeline and parallelization techniques applied to deep neural networks (DNN), making these type of AI applications much more efficient and reliable. This new approac

关键词： AI on-board computing on-board data processing high performance computing

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DocAssist: Signature Perception System using Deep Learning

DocAssist: Signature Perception System using Deep Learning

引用

2024 International conference on Computational Intelligence and Computing applications, ICCICA 2024

作者： Sharma, Pooja Miglani, Ishika Sharma, Lucky Panipat Institute Of Engineering And Technology Samalkha India Panipat Institute Of Engineering And Technology Artificial Intelligence And Machine Learning Samalkha India

ISBN: (纸本)9798350306446

All In the realm of communication, individuals employ two primary modes: written and spoken language. Handwriting, in particular, serves as a powerful tool for conveying information and emotions across various contexts, including healthcare. Despite its widespread use, deciphering handwritten text remains a challenge due to variations in individual writing styles and the lack of standardization. This challenge is particularly pronounced in the medical field, where handwritten prescriptions can lead to serious consequences if misinterpreted. Our research addresses this issue by proposing DocAssist, a novel model that utilizes Convolutional neural networks to achieve an impressive 81% accuracy in recognizing doctors' handwriting. By leveraging machine learning and image recognition technologies, DocAssist aims to enhance prescription legibility, mitigate medication errors, and ultimately improve patient safety and healthcare outcomes. Through rigorous experimentation with the EMNIST dataset, our study demonstrates the effectiveness of DocAssist in accurately interpreting handwritten prescriptions, highlighting its potential to revolutionize prescription processing and alleviate the burden on healthcare professionals and pharmacists. © 2024 IEEE.

关键词： image enhancement

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Innovative Methods for Low-Resolution image Recognition in Face and License Plate Tasks 4

Innovative Methods for Low-Resolution Image Recognition in F...

引用

4th International conference on Signal processing and Computer Science, SPCS 2023

作者： Li, Jiangqi Chen, Yuhan Wang, Xuelin Tan, Benying School of Artificial Intelligence Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781510672505

Despite the great performance achieved by deep learning-based image recognition techniques in recent years, low-resolution image recognition is still challenging in terms of improving model performance and reducing cost consumption for practical applications. In this task, we propose a solution to achieve low-resolution image recognition by appropriately reducing the resolution of the images in the database so that there is a better match between them and the image to be recognized. In the task of face recognition, we use the Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) to determine whether the image contains a face or not, followed by the KNN model for face information recognition. In the number plate recognition task, we use the Convolutional neural Network (CNN) model to recognize number plates. The experiments were validated on two publicly available datasets. The experimental results show that it can perform better in accomplishing low-resolution image recognition tasks while saving computational resources and other costs. © 2023 SPIE.

关键词： Convolutional neural networks

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ActivMedica—MRI Brain image Detection & Captioning with ActivGPT Integration

ActivMedica—MRI Brain Image Detection & Captioning with Act...

引用

International conference on Advances and applications of artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Malayil, Shanid Krishna, P. Sudeep Pattupara, Rabeen Mohamed, P. M. Rishil Faris, Salman Suhair, K. Muhammed Ahmed, P. Jemsheer MEA Engineering College Kerala Perinthelmanna India

ISBN: (纸本)9789819795062

ActivMedica is an innovator in brain tumor diagnosis in the rapidly changing healthcare industry, employing cutting edge AI tools including deep learning and natural language processing (NLP). The shortcomings of existing manual approaches for medical imaging data analysis include difficulty in identifying diverse brain tumors and increasing medical expenditures. Our approach, which at first is based on transfer learning and GIT architecture, uses the GIT convolutional neural network, or CNN, model. A carefully chosen and preprocessed diverse dataset, enhanced for variety and improved by transfer learning from the imageNet dataset for feature extraction, is essential for successful model training. Using the GIT-based methodology, ActivGPT, an AI chatbot built on OpenAI, enables real-time image analysis through natural language conversations. In addition to brain tumor detection, our research generates brief medical reports based on detected brain tumors, providing medical experts with comprehensive information. Brain tumor diagnosis is ethically improved by ActivMedica, a comprehensive healthcare innovation that combines AI, deep learning, and natural language processing. By doing this, diagnosis accuracy is greatly increased while moral and ethical requirements for AI use in healthcare are maintained. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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The Research of the Relationship Between artificial Intelligence and Human Brain 3

The Research of the Relationship Between Artificial Intellig...

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3rd International conference on artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Xin, Qiangwei School of Computer Science Xianyang Normal University Shaanxi Xianyang712000 China

ISBN: (纸本)9781643684444

artificial intelligence is a technology and method that utilizes computers and algorithms to simulate and implement human intelligence. It can learn and optimize through a large number of data and algorithms to achieve various intelligent tasks and functions, such as natural language processing, image recognition, intelligent decision-making, etc. artificial intelligence technology has been widely applied in fields such as healthcare, finance, transportation, and manufacturing, becoming an important force driving economic and social development. artificial intelligence is closely related to the human brain. This article analyzes the relationship between artificial intelligence and the human brain, including their connections and differences, imitation mechanisms, neural networks, and deep learning. © 2023 The Authors.

关键词： Brain

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Performance Assessment of Fourier Convolutional neural networks in Medical image Analysis for Breast Cancer Diagnosis 10

Performance Assessment of Fourier Convolutional Neural Netwo...

引用

10th International conference on Fuzzy Systems and Data Mining, FSDM 2024

作者： Lowmunkhong, Songkiat Panphuech, Anan Janthawanno, Patthra Onjun, Ratapong Kaennakham, Sayan Department of Interdisciplinary Science and Internationalization Institute of Science Suranaree University of Technology Nakhon Ratchasima30000 Thailand School of Mathematics and Geoinformatics Institute of Science Suranaree University of Technology Nakhon Ratchasima30000 Thailand

ISBN: (纸本)9781643685694

Breast cancer remains a major global health concern, and early diagnosis is crucial for improving patient outcomes. This study explores the performance of Fourier Convolutional neural networks (FCNNs) in comparison to traditional Convolutional neural networks (CNNs) like VGG16 for classifying breast cancer using ultrasound images. We evaluated the models on key performance metrics, including accuracy, precision, recall, and AUC-ROC, while also considering computational factors such as training time and memory consumption. Results show that VGG16 consistently delivers stable performance across different learning rates, while FCNN16 significantly outperforms VGG16 at a lower learning rate of 0.0001, achieving near-perfect classification metrics. These findings highlight the potential of FCNNs for breast cancer diagnosis, though further research is necessary to fully optimize this approach. © 2024 IOS Press. All rights reserved.

关键词： Convolutional neural networks

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Fused image Classification using Pre-trained Deep Convolutional neural networks 3

Fused Image Classification using Pre-trained Deep Convolutio...

引用

3rd International conference on artificial Intelligence and Smart Energy, ICAIS 2023

作者： Patil, Milind S. Mane, Pradip B. Institute of Information Technology All India Shri Shivaji Memoril Society's Pune India Vishwakarama Institute of Information Technology E and Tc Engineering Department Pune India

ISBN: (纸本)9781665462167

image Quality Assessment (IQA) has got importance in the computer vision applications as it provides tool to evaluate and rate different image processing algorithms. image Fusion is a process in which information from multiple images is combined into a single image. Due to specific nature of fused images present IQA methods have limitations for evaluation of image Fusion algorithms. With the recent development of Deep Convolutional neural networks (Deep CNNs), No- reference image quality assessment is becomes reality. This article has proposed the pre-trained Deep CNNs based image fusion classification using Alexnet, VGG19, Inception V3 and ResNet-50. Four states-of-the-art image fusion algorithms used for image fusion are Laplacian Pyramid (LP), Shift Invariant DWT (SIDWT), Discrete Wavelet Transform (DWT) and Ratio Pyramid (RP). To achieve the effective IQA, sufficiently large dataset of synthetically fused images is created and same is evaluated by using Deep CNNs. The results show that recent deep CNN methods correctly classify the fused images into corresponding categories based on its fusion algorithms. The results are consistent with FR-IQA methods. ResNet-50 provides best classification accuracy with less number of epochs and time to converge due to sparse network connections. © 2023 IEEE.

关键词： image classification

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Style Transfer: From Stitching to neural networks 5

Style Transfer: From Stitching to Neural Networks

引用

5th International conference on Big Data and artificial Intelligence and Software Engineering, ICBASE 2024

作者： Xu, Xinhe Wang, Zhuoer Zhang, Yihan Liu, Yizhou Wang, Zhaoyue Xu, Zhihao Zhao, Muhan Luo, Huaiying University of Illinois Urbana-Champaign Urbana United States Washington University in St. Louis St. Louis United States Georgia Institute of Technology Atlanta United States Carnegie Mellon University Pittsburgh United States Cornell University Ithaca United States

ISBN: (纸本)9798331506612

This article compares two style transfer methods in image processing: the traditional method, which synthesizes new images by stitching together small patches from existing pattern images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely to the background. The traditional method excels in creating artistic abstractions but can struggle with seamlessness, whereas the machine learning method preserves the integrity of foreground elements while enhancing the background, offering improved aesthetic quality and computational efficiency. Our study indicates that machine learning-based methods are more suited for real-world applications where detail preservation in foreground elements is essential. © 2024 IEEE.

关键词： Adversarial machine learning

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A Low-Cost Device for the Monitoring and Detection of Falls in Older Adults

A Low-Cost Device for the Monitoring and Detection of Falls ...

引用

International conference on Communication and Applied Technologies (ICOMTA)

作者： Calva-Bravo, B. Rodas-Perez, W. Robles-Bykbaev, V. Leon-Gomez, P. Univ Politecn Salesiana GI IATa Catedra UNESCO Tecnol Apoyo Inclus Educ Cuenca Ecuador

ISBN: (纸本)9789811963469;9789811963476

According to the World Health Organization (WHO), falls are the second cause of death due to accidental injuries, and older adults are the ones who suffer the most from them. In Ecuador, there are about 1,300,000 older adults, and falls are a major problem for their quality of life. For this reason, in this article, we present a low-cost prototype system for the monitoring and detection of falls, with the aim of providing support for the care of older adults. This tool is based on a module that applies computer vision and image processing techniques, convolutional neural networks (CNNs) and Web and mobile applications. They allow the monitoring and control of falls. To test the operation of the system, tests were carried out with fifteen volunteers. It was determined that the system managed to correctly detect 80% of fall-related events.

关键词： Convolutional neural networks Elderly people Fall detection Computer vision

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WiGNet: Windowed Vision Graph neural Network

WiGNet: Windowed Vision Graph Neural Network

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2025 IEEE/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Spadaro, Gabriele Grangetto, Marco Fiandrotti, Attilio Tartaglione, Enzo Giraldo, Jhony H. University of Turin Italy Institut Polytechnique de Paris Ltci Télécom Paris France

ISBN: (纸本)9798331510831

In recent years, Graph neural networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of-the-art performance in several computer vision tasks. However, their practical applicability is hindered by the computational complexity of constructing the graph, which scales quadratically with the image size. In this paper, we introduce a novel Windowed vision Graph neural Network (WiGNet) model for efficient image processing. WiGNet explores a different strategy from previous works by partitioning the image into windows and constructing a graph within each window. Therefore, our model uses graph convolutions instead of the typical 2D convolution or self-attention mechanism. WiGNet effectively manages computational and memory complexity for large image sizes. We evaluate our method in the imageNet-1k benchmark dataset and test the adaptability of WiGNet using the CelebA-HQ dataset as a downstream task with higher-resolution images. In both of these scenarios, our method achieves competitive results compared to previous vision GNNs while keeping memory and computational complexity at bay. WiGNet offers a promising solution toward the deployment of vision GNNs in real-world applications. We publicly released the code and pre-trained models at https://***/EIDOSLAB/WiGNet. © 2025 IEEE.

关键词： NP-hard

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