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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 6232 条记录，以下是431-440 订阅

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Deep learning-Based Semantic Segmentation of Blood Cells from Microscopic images 2nd

Deep Learning-Based Semantic Segmentation of Blood Cells fro...

引用

2nd International conference on Big Data, machine learning, and applications, BigDML 2021

作者： Asha, S.B. Gopakumar, G. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819934805

Morphological analysis and differential cell counting are important in characterizing many diseases including malaria and leukaemia. The basic building block involved is cell segmentation and is a challenging but beneficial task in cytopathology. Microscopy being the gold standard for cell analysis, approaches have been discovered from traditional image processing operations to deep learning techniques for cell segmentation from microscopy images. In the last few years, classification networks were extended for image segmentation using the pixel-based classification method, known as semantic segmentation. Convolutional neural networks exhibited good performance in image segmentation. However, the networks suffered some limitations due to fully connected layers and pooling layers that restricted the size of images to be given as input and resulted in the loss of spatial context. In this research, experiments were carried out with two popular CNN architectures UNet and SegNet, traditionally used for semantic segmentation of natural images. By identifying the capacity of these networks for cell segmentation on natural images, we have experimented on a custom-built RBC, WBC and platelet cell segmentation dataset based on ALL-IDB. We critically evaluate the performance of both architectures with an intuitive explanation of their difference in performance. The UNet outperformed SegNet that too with limited labelled training data giving a promising Dice score of 0.97. With the experiments and analysis conducted in this work, we propose that the UNet is a very good choice for cell segmentation in cytopathology applications. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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Bearing Fault image Recognition Method Based on Decision Fusion and Optimization Deep learning Framework 2

Bearing Fault Image Recognition Method Based on Decision Fus...

引用

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

作者： Yan, Hanjun Software Engineering Institute Jiangxi University of Software Professional Technology Nanchang China

ISBN: (纸本)9798350360240

Classical algorithms in data mining have been widely studied and applied in industrial equipment condition monitoring. However, the traditional deep learning models are not yet accurate in identifying the bearing status, and most of them rely on manual experience for identification, which leads to certain errors. This study proposes a hyperparameter optimization method using a swarm optimization algorithm for CNN architecture. At the beginning, the primary vibration signal is converted into a two-dimensional time-frequency map to construct a dataset. Second, the network noise immunity is tested and the optimal network framework is determined. Then, the parameter search is optimized using the swarm optimization algorithm and the fitness function is designed. Finally, the interpretability and stability of the network are improved by visualization and decision fusion. After the verification of Case Western Reserve University bearing dataset, the method introduced in this research paper displays noteworthy accuracy in fault diagnosis and holds substantial significance for engineering applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Generative Adversarial Network for image Synthesis: A Comprehensive Approach

Exploring Generative Adversarial Network for Image Synthesis...

引用

2024 International conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Pati, Natasha Sharma, Atul Gourisaria, Mahendra Kumar Bilgaiyan, Saurabh Patra, Sudhansu Shekhar Pattnayak, Parthasarathi KIIT Deemed to be University School of Computer Engineering Odisha Bhubaneswar India KIIT Deemed to be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350378092

In recent years, computer vision and machine learning have turned their attention to the research of using deep neural networks to produce realistic images. By using a sizable image collection to train the model, images can be produced at the pixel level. Creating anime images from a basic drawing presents an interesting research challenge. The domain of anime picture generation is studied in this article using the Generative Adversarial networks (GAN) model. Unsupervised machine learning algorithms use a subset of artificial intelligence called GAN. Using a discriminator and generator, the model can create realistic anime faces. While the Generator is a network that produces images, the Discriminator determines if the image is produced or real. To produce anime faces of the finest quality, it places the highest priority on the thorough selection of superior datasets. Strict training is carried out using sophisticated methods and optimized hyper-parameters. Additionally, a comparative analysis with other generative picture creation methods is provided by the research. © 2024 IEEE.

关键词： Generative adversarial networks

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Scene Recognition Based on An Improved DenseNet with Attention Mechanism 11

Scene Recognition Based on An Improved DenseNet with Attenti...

引用

11th International conference on machine Intelligence Theory and applications, MiTA 2024

作者： Li, Angran Sun, Mingzhu Nankai University College of Artificial Intelligence Tianjin China

ISBN: (纸本)9798350385670

High-resolution remote sensing image classification is of great significance to scene recognition and plays a pivotal role in surface cover classification, environmental monitoring, geological exploration and resource management. Convolutional neural network (CNN) has a powerful feature learning capability of automatic extraction of features and can categorize different scenes with high accuracy in scene recognition. In this study, we train on the Aerial image Dataset (AID) and reprocess it with data augmentation. We enhance feature focus within densely connected blocks by incorporating Efficient Channel Attention (ECA), thereby proposing the ECA-DenseNet architecture. Moreover, we employ transfer learning and pre-train the network to accelerate the training process. The accuracy, precision, recall and f1 score of the final model reached 9 7. 1 %, 9 7. 0 %, 9 6. 8 % and 9 6. 9 %, respectively. The experimental results show that ECA-DenseNet fully utilizes the different features extracted from each convolutional kernel and captures the key information in the scene images more effectively, which effectively improves the classification accuracy of scene recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Multimodal Medical image Segmentation Algorithm Based on Convolutional neural networks 2

Multimodal Medical Image Segmentation Algorithm Based on Con...

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2nd IEEE International conference on networks, Multimedia and Information Technology, NMITCON 2024

作者： Han, Siyuan Wang, Yao Wang, Qian Chongqing Medical University Intelligent Medical Engineering Chongqing China Nanjing University Nanjing Stomatological Hospital Affiliated Hospital of Medical School Research Institute of Stomatology Nanjing China Qiannan Prefecture Hospital of Traditional Chinese Medicine Department of Gynaecology Jianjiang Middle River Road Guizhou Duyun China

ISBN: (纸本)9798350372892

In this research, we introduce an enhanced Convolutional neural Network (CNN) model designed to address the limitations of traditional CNNs in processing multimodal medical images, such as inadequate data fusion and imprecise feature extraction. By employing advanced deep learning techniques, our model significantly improves the fusion and feature extraction processes of multimodal data, leading to a marked increase in the accuracy and efficiency of medical image segmentation. Through cross-validation tests conducted on three diverse datasets, our enhanced CNN model demonstrated an average segmentation accuracy between 92% and 94%, outperforming established models like U-Net and V-Net. Moreover, the model shows exceptional robustness across different noise levels, maintaining a high segmentation accuracy of 95% in noise-free conditions and 81% under high noise scenarios. These results underscore the superior performance and reliability of our improved CNN model in medical image segmentation, showcasing its potential for advancing multimodal medical image processing techniques in clinical applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Fusion of Traditional and Deep learning Models for Underwater image Quality Assessment 8

Fusion of Traditional and Deep Learning Models for Underwate...

引用

8th IEEE International conference on Information and Communication Technology, CICT 2024

作者： Dhivya, R.M. Nagasai, V. Masilamani, V. Indian Institute of Information Technology Design and Manufacturing Department of Computer Science and Engineering Kancheepuram Chennai600127 India

ISBN: (纸本)9798331531539

Underwater image quality is significantly affected by the unique challenges of the underwater environment, including light attenuation, color distortion, backscatter, and the presence of particulate matter. These factors reduce visibility, alter color perception, and introduce noise and artifacts that degrade the overall quality of captured images. Ensuring high-quality underwater images is essential for various applications, including marine biology, environmental monitoring, underwater navigation, and industrial operations. This research proposes a novel approach to evaluating underwater image quality by integrating conventional machine learning methods with advanced deep learning architectures. The fusion process, employing Support Vector Regression (SVR), known as FTDUIQA (Fusion of Traditional and Deep learning Models for Underwater image Quality Assessment), combines well-established deep learning-based models - CNNIQA (Convolutional neural Network for No-Reference image Quality Assessment), DBCNN (Deep Bilinear Convolutional neural Network), and HyperIQA (HyperNet for image Quality Assessment) - with the classical IQA model, MSAEQA (Multi-scale and Antagonistic Energy Distribution). Additionally, we incorporate a modified deep learning model, MAUIQA (Multi-scale AttentionNet for Underwater image Quality Assessment), which has been adapted from its image super-resolution application. This hybrid approach leverages the strengths of both deep learning and traditional techniques to provide a robust and comprehensive assessment of underwater image quality. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated design of Convolutional neural Network architecture using Gray Wolf Optimization for plant seedlings classification 8

Automated design of Convolutional Neural Network architectur...

引用

8th IEEE International conference on image and Signal processing and their applications, ISPA 2024

作者： Badis, Lamis Aliouat, Wahiba Bouchiba, Kenza Faculty of Sciences and Applied Sciences Department of Computer Sciences University of Bouira Algeria Faculty of Sciences and Applied Sciences LIM Laboratory Department of Computer Sciences University of Bouira Algeria

ISBN: (纸本)9798350309249

Convolutional neural networks (CNNs) have gained significant popularity in image classification tasks, yet achieving their optimal design remains a challenge due to the vast array of possible layer configurations and associated hyperparameters. Selecting the best CNN model for a given task often demands considerable time investment in training numerous models. To address this issue, we propose an automated method for CNN architecture design, utilizing pretrained models like the Backbone and employing Gray Wolf Optimization. This approach automatically generates and evaluates candidate CNN architectures for classifying plant seedlings. Our objective is to distinguish between weed and crop seedlings. Additionally, we introduce a gray wolf representation to encode CNN architectures and their hyperparameters. Our method combines the strengths of transfer learning from pre-trained models to extract meaningful image features with the optimization capabilities of the Gray Wolf Optimization (GWO) algorithm. By leveraging these techniques, our method achieves exceptional accuracy, surpassing state-of-the-art methods with a validation accuracy of up to 97.83%. This innovative approach offers a transformative tool for enhancing the accuracy of CNN models, tailored specifically to the dataset at hand. © 2024 IEEE.

关键词： Convolutional neural networks

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Building Damage Assessment in Conflict Zones: A Deep learning Approach Using Geospatial Sub-Meter Resolution Data 6

Building Damage Assessment in Conflict Zones: A Deep Learnin...

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

作者： Risso, Matteo Goffi, Alessia Motetti, Beatrice Alessandra Burrello, Alessio Bove, Jean Baptiste Macii, Enrico Poncino, Massimo Pagliari, Daniele Jahier Maffeis, Giuseppe Politecnico di Torino Italy TerrAria S.R.L. Italy Croce Rossa Italiana Italy

ISBN: (纸本)9798331506520

Very High Resolution (VHR) geospatial image analysis is crucial for humanitarian assistance in both natural and anthropogenic crises, as it allows to rapidly identify the most critical areas that need support. Nonetheless, manually inspecting large areas is time-consuming and requires domain expertise. Thanks to their accuracy, generalization capabilities, and highly parallelizable workload, Deep neural networks (DNNs) provide an excellent way to automate this task. Nevertheless, there is a scarcity of VHR data pertaining to conflict situations, and consequently, of studies on the effectiveness of DNNs in those scenarios. Motivated by this, our work extensively studies the applicability of a collection of state-of-the-art Convolutional neural networks (CNNs) originally developed for natural disasters damage assessment in a war scenario. To this end, we build an annotated dataset with pre- and post-conflict images of the Ukrainian city of Mariupol. We then explore the transferability of the CNN models in both zero-shot and learning scenarios, demonstrating their potential and limitations. To the best of our knowledge, this is the first study to use sub-meter resolution imagery to assess building damage in combat zones. © 2025 IEEE.

关键词： Zero-shot learning

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Quaternion neural Network Architectures in Various Application Domains

Quaternion Neural Network Architectures in Various Applicati...

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2023 International conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Tripathi, Bharat Srivastava, Nidhi Tiwar, Amod Kumar Sharma, Abhishek Amity University Department of Aiit Uttar Pradesh Lucknow India Rajkiya Engineering College Department of Computer Science Uttar Pradesh Sonbhadra India Scott World School Scott World School Kanpur India

ISBN: (纸本)9798350302820

On real-world applications like image processing, speech reorganization, and signal processing, QNN have significantly outperformed real-valued neural networks. This survey contains an overview of the most recent and ongoing research on quaternion neural networks and their applications in diverse disciplines. For each QVNN that has been proposed, the methods, algorithms, and applications described in the paper. © 2023 IEEE.

关键词： Deep learning

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A Review On Fusion Of Spiking neural networks And Transformersc 6

A Review On Fusion Of Spiking Neural Networks And Transforme...

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

作者： Marsi, Oumaima Ambellouis, Sebastien Mennesson, Jose Meurie, Cyril Fleury, Anthony Tatkeu, Charles Univ. Gustave Eiffel COSYS LEOST IMT Nord Europe Institut Mines-Télécom LilleF-59000 France Univ. Gustave Eiffel COSYS LEOST LilleF-59000 France Univ. Lille Centre for Digital Systems IMT Nord Europe Institut Mines-Télécom LilleF-59000 France

ISBN: (纸本)9798331506520

This paper provides a comprehensive review of the integration of Spiking neural networks (SNNs) and Transformers, combining the energy efficiency of SNNs with the high performance of Transformer architectures. By leveraging the event-driven nature of SNNs and the powerful self-attention mechanism of Transformers, this fusion aims to address the challenges of high energy consumption in deep learning while improving task accuracy, especially for complex datasets. We introduce the core concepts of SNNs and Transformers, reviewing state-of-the-art methods for their combination, including hybrid architectures. The performance of each architecture is presented thanks to both static and neuromorphic datasets, highlighting their advantages and limitations. This review also discusses the challenges of integrating self-attention into spiking architectures and outlines future research directions to further enhance model performance and energy efficiency. © 2025 IEEE.

关键词： Distribution transformers

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