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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 6125 条记录，以下是241-250 订阅

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RECURRENT NERUAL IMAGING: AN EVOLUTIONARY APPROACH FOR MixED POSSION-GAUSSIAN image DENOISING 21

RECURRENT NERUAL IMAGING: AN EVOLUTIONARY APPROACH FOR MIXED...

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21st IEEE International conference on machine learning and applications (IEEE ICMLA)

作者： Ranganath, Aditya DeGuchy, Omar Santiago, Fabian Singhal, Mukesh Marcia, Roummel Univ Calif Merced Appl Math Merced CA 95343 USA Univ Calif Merced Sch Engn Merced CA 95343 USA

ISBN: (纸本)9781665462839

Recurrent neural networks (RNNs) are traditionally used for machine learning applications for temporal sequences such as natural language processing. Its application to image processing is relatively new. In this paper, we apply RNNs to denoise images corrupted by mixed Poisson and Gaussian noise. The motivation for using an RNN comes from viewing the denoising of the Poisson-Gaussian realization as a temporal process. The network then attempts to trace back the steps that create the noisy realization in order to arrive at the noiseless reconstruction. Numerical experiments demonstrate that our proposed RNN approach outperforms convolutional autoencoder methods for denoising and upsampling low-resolution images from the CIFAR-10 dataset.

关键词： Recurrent neural networks image denoising upsampling autoencoders

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A Short Survey on Deep learning-Based Frameworks for Knowledge Graph applications 2

A Short Survey on Deep Learning-Based Frameworks for Knowled...

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2nd International conference on Computer Technologies, ICCTech 2023

作者： Kushk, Abdulkarim Kochut, Krzysztof School of Computing University of Georgia AthensGA United States

ISBN: (纸本)9781665455824

Knowledge Graphs (KG) are repositories of structured, machine-readable data stored as relational triples. DBpedia, Freebase, and YAGO are examples of KGs that have been playing a vital role in many applications, including question answering systems, recommendation systems, and expert systems. Deep learning is a subset of machine learning techniques that consists of layers of artificial neural networks. It is a relatively young technology that, in a very short amount of time, has achieved state-of-the-art performance in many fields of science, business, and government. For instance, convolutional networks have achieved major breakthroughs in processing image, video, and audio files. Moreover, recurrent networks have a similar effect in processing sequential data, such as texts and time series [1]. In this survey, we shed light on the development deep learning has brought about in two KG tasks: entity summarization and entity linking. Experiments on standard performance measures and benchmark datasets show that deep learning-based models generally outperform other models by a considerable margin. © 2023 IEEE.

关键词： Semantic Web

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Leveraging GANs for Synthetic Tabular Data Generation: A Platform-Centric Solution to Data Scarcity 26

Leveraging GANs for Synthetic Tabular Data Generation: A Pla...

引用

26th Irish machine Vision and image processing conference, IMVIP 2024

作者： Mobeen, Muhammad Afzal, Awais Saeed, Reeda Arshad, Tayyaba Asad, Muhammad Institute of Space Technology Islamabad Pakistan University of Galway Ireland

ISBN: (纸本)9781837242672

In an era dominated by data-driven decision-making, the insufficiency of high-quality and diverse datasets presents a profound challenge for researchers, businesses, and organizations. The scarcity of data, particularly in certain domains, constrains the application of Artificial Intelligence (AI) and machine learning, hindering progress and innovation. This project endeavours to alleviate this bottleneck through the development of Synthium AI, a platform that provides easy access to advanced Synthetic Tabular Data Generation Deep neural Network Models. Synthium AI leverages Generative Adversarial networks (GANs) to perform Synthetic Tabular Data Generation (STDG), focusing on a wide range of tabular datasets. By democratizing access to high-quality synthetic data, Synthium AI empowers stakeholders to overcome the challenges posed by data scarcity, facilitating innovation and progress in various domains. As AI applications become increasingly prevalent, the ability to generate synthetic datasets emerges as an enabler, bridging the gap created by data limitations and fostering advancements in AI research and applications. While the current implementation focuses on tabular data, future work will extend Synthium AI's capabilities to address challenges in computer vision and image processing. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Generative adversarial networks

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Text Recognition in Persian Documents Using Self-Supervised learning 6

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

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6th IEEE International conference on image processing, applications and Systems, IPAS 2025

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

ISBN: (纸本)9798331506520

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. © 2025 IEEE.

关键词： Self-supervised learning

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Convolutional neural network is used in target contour extraction

Convolutional neural network is used in target contour extra...

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2024 International conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Bi, Mingxue Lu, Na College of Equipment Engineerings Shenyang Ligong University Liaoning Shenyang110168 China

ISBN: (纸本)9781510689176

Target contour extraction is a key task in the field of image processing, which is of great significance for applications such as image segmentation, object detection, and scene understanding. Traditional methods are often limited by complex scenarios and changing target shapes, and their effects may be unstable. In recent years, with the vigorous development of deep learning technology, Convolutional neural Network (CNN) has shown significant advantages in target contour extraction tasks due to its powerful feature representation and learning *** this paper, the loss function is used, and the loss function mechanism adopted is Softmax, which is especially suitable for multi-classification problems and is widely used in supervised and unsupervised learning scenarios. © 2025 SPIE.

关键词： Convolutional neural networks

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A Survey on Adversarial Text Attacks on Deep learning Models in Natural Language processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

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5th International conference on Data Science, machine learning and applications

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

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Research on Deep learning Model of Feature Extraction Based on Convolutional neural Network 2

Research on Deep Learning Model of Feature Extraction Based ...

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2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Liu, Houze Li, Iris Liang, Yaxin Sun, Dan Yang, Yining Yang, Haowei New York University Department Computer Science and Engineering New York United States Graduate School of Art & Science New York University New York United States Viterbi School of Engineering University of Southern California Los Angeles United States Washington University Department of Electrical and Systems Engineering St. Louis United States School of Computer Science Carnegie Mellon University Pittsburgh United States University of Houston Cullen College of Engineering Houston United States

ISBN: (纸本)9798350360240

neural networks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep neural networks also have a large demand for computing resources, which may cause convolutional neural networks to be unable to be implemented on terminals. Therefore, this paper will carry out the optimal classification of convolutional neural networks. Firstly, according to the characteristics of pneumonia images, AlexNet and InceptionV3 were selected to obtain better image recognition results. Combining the features of medical images, the forward neural network with deeper and more complex structure is learned. Finally, knowledge extraction technology is used to extract the obtained data into the AlexNet model to achieve the purpose of improving computing efficiency and reducing computing costs. The results showed that the prediction accuracy, specificity, and sensitivity of the trained AlexNet model increased by 4.25 percentage points, 7.85 percentage points, and 2.32 percentage points respectively. The graphics processing usage has decreased by 51% compared to the InceptionV3 mode. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Enhancement of Covid-19 Chest X-Ray image Classification Using GAN and CNN 7

Performance Enhancement of Covid-19 Chest X-Ray Image Classi...

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7th International conference on machine Vision and applications (ICMVA)

作者： Panday, Sanjeeb Prasad Dumre, Mani Prasad Shakya, Aman Joshi, Basanta Tribhuvan Univ Inst Engn Dept Elect & Comp Engn Pulchowk Campus Lalitpur Nepal Nepal Telecom Kathmandu Nepal

ISBN: (纸本)9798400716553

Covid-19, a respiratory disease caused by the SARS-CoV-2 virus, manifests in individuals with varying degrees of severity. Chest X-rays serve as initial screening procedures for suspected Covid-19 infections, aiding in the detection of abnormalities. Various approaches utilizing deep learning models like Convolutional neural networks (CNN) have been proposed for Covid-19 detection through the analysis of chest radiograph images. However, the availability of radiographic Chest X-ray images for Covid-19 remains limited and not easily accessible to the global research community. This scarcity of data poses a significant challenge for further research in the diagnosis of Covid-19 using radiographic images. This research aims to overcome this obstacle by the generation of synthetic Chest X-ray images through the use of Generative Adversarial networks (GANs) which produce more realistic images compared to traditional data augmentation methods like rotation, scaling, and flipping. The research analyzes and compares the results to address the limited availability of Covid-19 Chest X-ray data. By augmenting the existing dataset, this research has aimed to improve the performance of Covid-19 diagnosis models based on past research findings.

关键词： SARS-CoV-2 Chest X-ray data augmentation generative adversarial network (GAN) Convolutional neural networks (CNNs) Transfer learning

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HPDNET: Hyper Prior Dependent Demosaic neural Network 6

HPDNET: Hyper Prior Dependent Demosaic Neural Network

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6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Seo, Youngil Jun, Sungho Lim, Dongpan Song, Seongwook Samsung Electronics System LSI Division Yongin Korea Republic of

ISBN: (纸本)9798331506520

Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are often characterized by heavy computational requirements, rendering them unsuitable for deployment on resource-constrained platforms. This presents a critical challenge in the field of image demosaicking: while deep learning approaches excel in enhancing image quality, their computational intensity poses a significant hindrance to their wider adoption. Consequently, there is a pressing need for methodologies that can strike an optimal balance between achieving superior image quality and maintaining computational efficiency. In this work, we propose a new deep framework, hyper-prior dependent demosaic neural network, HPDNet that utilizes the significant concepts of the conventional algorithm and the characteristic of image sensor data. We designed the network that exploits three concepts, those are pixel gradient prior attention, phase separation, and multi-level sparse and dense feature extraction. We designed the deep neural network that extracts the optimal gradient prior and the multi-level extracted features are fused and attended by gradient prior. It can fully utilize spatially variant information. Also to make the network deployed in the mobile platform, we devised self-pruned image convolution that adopts image filter characteristic and reduce computations. Experiments show that proposed network outperforms SOTA demosaic networks both in terms of image quality and computation. © 2025 IEEE.

关键词： image quality

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Advanced machine vision techniques for real-time quality detection and grading of navel oranges using high-resolution imaging and deep learning algorithms

Advanced machine vision techniques for real-time quality det...

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International conference on Computer Vision, Robotics, and Automation Engineering, CRAE 2024

作者： Zhang, Xiaoyan Beijing Institute of Technology Zhuhai Zhuhai519000 China

ISBN: (纸本)9781510682283

In recent years, the rapid development of computer vision and artificial intelligence has significantly advanced agricultural applications, particularly in the quality detection and grading of navel oranges. This review explores the latest advancements in machine vision technologies for navel orange quality assessment, focusing on image processing, deep learning algorithms, and multispectral imaging. image processing techniques, fundamental to machine vision systems, enable the extraction of visual features such as color, shape, and texture, enhancing detection accuracy. Recent studies have demonstrated the efficacy of deep learning algorithms, particularly convolutional neural networks (CNNs), in achieving high-precision grading and real-time performance. Furthermore, multispectral and hyperspectral imaging technologies offer rich spectral information, facilitating more accurate quality detection and maturity assessment. Despite significant progress, challenges such as complex natural environments, lighting conditions, and the high cost of imaging equipment persist. Future research directions include integrating multi-source data fusion, developing efficient deep learning algorithms, and promoting cost-effective imaging technologies. Our proposed advanced algorithm employs the DAHENG machine vision experimental platform and MER-132-30UC camera for high-resolution image acquisition, with HALCON software for sophisticated image processing and real-time classification. This system enhances accuracy and efficiency in navel orange quality detection and grading, addressing key challenges in current agricultural practices. © 2024 SPIE.

关键词： Citrus fruits

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