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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81-90 订阅

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Comparing Time Series Assist Vector machines and Recurrent neural networks for Hyper Spectral image Popularity 5th

Comparing Time Series Assist Vector Machines and Recurrent N...

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5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Goswami, Shubhashish Pasha, Afroz Mishra, Awakash Krishnamoorthy, Ramkumar Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

This paper examines the overall performance of two system mastering models, Time series help Vector machines (SVMs) and Recurrent neural networks (RNNs), on classification of Hyper Spectral photographs (HSIs). The fashions are in comparison based totally on their recognition accuracy which includes the time requirements to build fashions. The paper additionally evaluates two varieties of preprocessing techniques: minimum Redundancy maximum Relevancy (MRMR) and precept aspect evaluation (PCA). The effects of the experiments highlight that SVMs and RNNs perform considerably distinct for HSI class and that counting on one version simplest may lead to exclusive type performance. Moreover, the experiments show that with increasing complexity, the quantity of time required to assemble the RNNs will increase significantly. The comparisons in the paper allow further exploration in the discipline and help the choice of the high-quality model with the aid of capacity practitioners and developers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Deep Joint Unrolling for Deblurring and Low-Light image Enhancement (JUDE)

Deep Joint Unrolling for Deblurring and Low-Light Image Enha...

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

作者： Vo, Tu Park, Chan Y. Kc Machine Learning Lab

ISBN: (纸本)9798331510831

Low-light and blurring issues are prevalent when capturing photos at night, often due to the use of long exposure to address dim environments. Addressing these joint problems can be challenging and error-prone if an end-to-end model is trained without incorporating an appropriate physical model. In this paper, we introduce JUDE, a Deep Joint Unrolling for Deblurring and Low-Light image Enhancement, inspired by the image physical model. Based on Retinex theory and the blurring model, the low-light blurry input is iteratively deblurred and decomposed, producing sharp low-light reflectance and illuminance through an unrolling mechanism. Additionally, we incorporate various modules to estimate the initial blur kernel, enhance brightness, and eliminate noise in the final image. Comprehensive experiments on LOL-Blur and Real-LOL-Blur demonstrate that our method outperforms existing techniques both quantitatively and qualitatively. © 2025 IEEE.

关键词： Deep neural networks

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Progressive Enhancement of Anatomical Structural and Medical Feature learning for Cephalometric Landmark Detection 24

Progressive Enhancement of Anatomical Structural and Medical...

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7th International conference on Digital Medicine and image processing, DMIP 2024

作者： Li, Mengqi Qi, Senrong Zhu, Xuemei Niu, Kai Han, Jianhui Li, Ying He, Zhiqiang Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China Beijing Stomatological Hospital & School of Stomatology Capital Medical University Beijing China Beijing University of Posts and Telecommunications Hospital Beijing China

ISBN: (纸本)9798400709586

Landmark detection is an essential foundation of cephalometric analysis, playing a crucial role in clinical diagnosis and treatment of orthodontic and orthognathic conditions. This paper proposes a novel framework that enables targeted learning and extraction of structural and medical features based on neural network. Specifically, our framework's key components comprise Contour Sharpener, Coupling Transformer, and Progressively Targeted Feature Extraction networks (PT-FENet). The Contour Sharpener intensifies contour features informed by the prior distributions of landmarks, enabling the network to learn anatomical structural features. The Coupling Transformer couples and transforms associated landmarks based on the similarity of features around medically related landmarks and the invariant structure of geometrically constrained landmarks to enhance the learning of anatomical structures and medical correlations among them. PT-FENet fully extracts multi-scale features and progressively targets the refinement of anatomical structural and medical features grounded in prior knowledge, thereby augmenting the attention towards detection information. Experimental results demonstrate that our method outperforms existing state-of-the-art methods on public dataset. Our method enables rapid batch processing of images for precise landmark detection, thus possessing significant value in clinical cephalometric analysis and orthodontic applications. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Batch data processing

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On the Detection of Retinal image Synthesis Obtained Through Generative Adversarial Network

On the Detection of Retinal Image Synthesis Obtained Through...

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18th International Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Di Giammarco, Marcello Santone, Antonella Cesarelli, Mario Martinelli, Fabio Mercaldo, Francesco Pisa Italy Department of Information Engineering University of Pisa Pisa Italy Department of Medicine and Health Sciences "Vincenzo Tiberio" University of Molise Campobasso Italy Department of Engineering University of Sannio Benevento Italy CS Rende Italy

Adversarial machine learning on medical imaging is one of the many applications for which the evaluation of Generative Adversarial networks in the medical field has demonstrated remarkable interest. This paper proposes a method in which Convolutional neural networks are trained and tested on the binary classification of real and fake images, generated through generative adversarial networks. In this paper, the considered experiments are on the RGB fundus retina images of the human eye. Results highlight networks with optimal performance, and completely recognize real/fake classification;however, on the other hand, other networks mis-classify the images, enhancing security and reliability problems. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Generative adversarial networks

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SHGNN: A Substructure-Aware and High Expressive Graph neural Network for Graph Classification 5th

SHGNN: A Substructure-Aware and High Expressive Graph Neural...

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5th International conference on neural Computing for Advanced applications (NCAA)

作者： Sun, Jinyong Dong, Zhiwei Sun, Zhigang Wen, Yimin Zhao, Xiang Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Graph neural networks (GNNs), with their remarkable capabilities in learning graph features, have achieved success in graph classification tasks. However, the Message Passing GNNs (MP-GNNs), sharing the feature aggregation mechanism with the 1-Weisfeiler-Leman (1-WL) graph isomorphism test, suffer from limited expressiveness in downstream tasks, such as graph classification. To enhance the expressive power of GNNs, we propose a substructure-aware and high expressive graph neural network model. Firstly, Ego substructures are extracted from a graph and fed into a MP-GNN to obtain substructure-level features. Then, a count-sensitive neighborhood hashing method is introduced to encode the structural characteristics of Ego substructures, which are then injected into the corresponding substructure-level features to enhance the model's expressiveness. Next, an Ego-substructure-aware Transformer is utilized to extract global features from the graph, which use a token to fuse the interaction information among graphs' nodes, thereby generating graph-level feature vector. Finally, we apply a Multi-Layer Perceptron (MLP) followed by a softmax layer to classify graphs. Theoretical analysis demonstrates that the proposed SHGNN ismore powerful than 1-WL test. Experimental results show that, compared to the state-of-the-art graph deep learning methods, our model not only outperforms 1-WL in expressiveness, but also achieves higher classification accuracy.

关键词： graph classification graph neural networks substructure-aware neighborhood hashing Transformer

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2nd International conference on Cyber Intelligence and Information Retrieval, CIIR 2023

2nd International Conference on Cyber Intelligence and Infor...

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2nd International conference on Cyber Intelligence and Information Retrieval, CIIR 2023

ISBN: (纸本)9789819776023

The proceedings contain 40 papers. The special focus in this conference is on Cyber Intelligence and Information Retrieval. The topics include: A Performance Analysis to Detect Synthetic images with Deep learning-Based image Classification;A Study of AI-Based Systems in the Judicial Interpretation of the Law;broken Authentication and Its Significance in Protecting Online applications: An Overview Paper;skin Lesion Segmentation Using Deep learning;AI in Subconscious Language learning for Error Remediation;amazon Sales Sentiment Prediction and Price Forecasting Using Facebook Prophet;house Price Prediction Using Random Forest Regression Considering the Nearest In-Demand Location;Enhancing Legal Document Understanding Through Text Summarization: A Study on NLP and Wavelet Tree Techniques;neural Network Participation to Enhance Hydrogen-Biofuel CI Engine Performance and Combat Emissions;Analyzing MACD Divergence: Challenges in Forex and Commodity Trading;GIS-Based Analysis of Water Integrity Differences Within the Madurai Region;a machine learning Approach to Cardiovascular Disease Prevention in Smart Healthcare;examination of Sustainability 4.0 with Special Reference to International Legal Doctrines;a Study into the Application of Ecological Drones for Wildlife Conservation;the Transformative Role of Artificial Intelligence in the Legal Profession and International Arbitration;impact of Health Information Systems on Healthcare Service Quality in Supporting Non-communicable Disease Management in Kosovo;contemporary Challenges in Metaverse: A Techno-Legal Analysis;artificial Intelligence in Disease Diagnostics: Rethinking Risk Factors for Cervical Cancer;an Analytical Survey on Credit Scoring Mechanism in India;the Study of the Effectiveness and Sensitivity of Stochastic Divergence in Foreign Exchange Market;conceptual Framework and Impact of Blended Education System on Employability Skills;adversarial Attacks on neural networks;preface.

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Partial Distributed Deep learning Inference Model for image based Edge Device Cluster 24

Partial Distributed Deep Learning Inference Model for Image ...

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8th International conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Xiao, Dun Wang, Xiaogang Yang, Zhihe Huang, Chao School of Electronic and Information Shanghai Dianji University Shanghai China

ISBN: (纸本)9798400710094

In recent years, the development of AI technology has driven the development of network edge applications such as smart manufacturing, smart factories, and smart cities. Deep neural networks are increasingly being deployed in smart manufacturing edge application scenarios to carry out edge detection tasks. However, there is a gap between the expensive DNN computing network and the limited computing power of the edge devices. To deploy compute-intensive neural networks on resource-constrained edge devices, traditional approaches rely either on offloading workloads to a remote central cloud or on optimizing compute on local edge devices. However, the method of uploading tasks to the central cloud is significantly affected by the unreliability and delay of the WAN, and the sensitive information of users is easy to be leaked during transmission, and the local computing method is also limited by the computing power. The latest machine learning models have great demands on memory, computing, and energy, which poses challenges to distributed computing and data aggregation for heterogeneous and resource-constrained edge devices. In order to address these challenges, this paper proposes solutions for different image types in intelligent manufacturing scenarios. When images are processed in the production process of intelligent manufacturing, this paper adopts the method of horizontal partitioning of the DNN model to perform DNN-based inference tasks on the edge device cluster. In order to reduce the consumption of network bandwidth, a dynamic priority scheduling algorithm is used to optimize the distribution of inference tasks at the edge nodes to improve the inference speed. Finally, experiments show that the edge node optimization scheduling method proposed in this paper has a good effect on the performance of running time, communication cost and energy consumption of edge devices. © 2024 Copyright held by the owner/author(s).

关键词： Smart manufacturing

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A Hybrid Optimized learning Framework for Compound Facial Emotion Recognition 5th

A Hybrid Optimized Learning Framework for Compound Facial Em...

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5th International conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Borgalli, Rohan Appasaheb Surve, Sunil Department of Electronics and Telecommunication Engineering Shah and Anchor Kutchhi Engineering College Chembur Mumbai400088 India Department of Computer Engineering Fr. Conceicao Rodrigues College of Engineering University of Mumbai Bandra Mumbai400050 India

ISBN: (纸本)9789819773701

Facial Emotion Recognition (FER) represents a critical domain within the realms of machine vision and artificial intelligence, finding widespread applications in both academic research and various industries. While there exist multiple sensors for conducting FER, recent studies emphasize the effectiveness of utilizing facial images/videos to discern emotions due to the rich information embedded in visual expressions. This study specifically addresses the task of detecting compound emotions using the iCV Multi-Emotion Facial Expression Dataset (iCV-MEFED) and explores different learning frameworks. In our initial approach, a deep learning Convolutional neural Network (CNN) model was employed to extract features from each image. Subsequently, a Multi-Class Support Vector machine (mSVM) classifier was utilized to identify the corresponding emotions. Our research presents two novel approaches for compound facial emotion recognition. The first methodology, a machine learning-based framework, achieved a 26% accuracy rate, which represents an improvement over previous state-of-the-art results. This approach demonstrated superior performance in misclassification analysis, with a 74.00% accuracy rate. The second methodology employs a deep learning CNN-based model enhanced by WCHGSO-DNFN optimization. This approach yielded a 28% accuracy rate on the iCV-MEFED dataset, again surpassing existing benchmarks. In misclassification comparisons, this method achieved a 72.00% accuracy rate, consistently outperforming competing approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Smart AI Tool for Accident Damage Detection 6th

Smart AI Tool for Accident Damage Detection

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Saseekala, M. Thomas, Atulya Faculty of Computer Applications Business Analytics Specialization School of Business and Management Christ University Bangalore India Christ University Bangalore India

ISBN: (纸本)9789819774258

Accidents and fatalities from motor vehicle accidents are major concerns despite substantial advancements in safety technology. Because of this, the industry has made significant investments in creating new safety features, like cutting-edge driver assistance systems, and raising public awareness of safe driving habits. In general, car accidents can result in severe damage to the vehicles involved, and assessing and repairing that damage can be time-consuming and expensive. Manual inspection of vehicles is prone to errors and often requires trained professionals to identify the extent and location of the damage. Therefore, there is a need to develop an automated system that can detect and assess the damage caused to vehicles using AI and deep learning techniques. An image-based processing technique, YOLOv3, is proposed in this work to automate damage detection on automobiles. In the work, we used CNN to create a Mask R-convolutional neural networks model to identify the location of damage on a car. The damaged area is precisely marked in the images. The base weights from the Mask R-CNN COCO dataset are used to train the model. 21 epochs are used to process the images. The surface of the damage is highlighted in the final image using a color splash technique after processing. Auto insurance firms, vehicle rental companies, and repair shops would all benefit from this automated method of determining the degree of exterior vehicle damage and then calculating the severity of that damage. The value of fraudulent auto insurance claims can also be reduced. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Exploring the Performance of Meta learning Strategies for Medical image Segmentation with Transfer learning 5th

Exploring the Performance of Meta Learning Strategies for Me...

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5th International conference on Data Science, machine learning and applications, ICDSMLA 2023

作者： Yadav, Dhyan Chandra Pandey, Vijay Kumar Ranjan, Vivek Karthikeyan, M.P. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Mechanical Engineering Vivekananda Global University Jaipur India Department of Computer Science Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

This paper explores the overall performance of meta-gaining knowledge of strategies for medical photo segmentation with switch mastering. It discusses the latest advancements in deep gaining knowledge of and switching learning strategies and their software to clinical photo segmentation. Specifically, the examiner evaluates meta-studying strategies, especially for clinical image segmentation, which aim to optimize the parameters of conventional transfer learning strategies. First, the paper opinions the unique varieties of switch learning strategies, which include pre-skilled models, area version, transfer studying inside the convolution neural networks (CNNs), and multi-undertaking getting to know, and affords an outline in their packages. Then, numerous meta-studying strategies are mentioned, including reinforcement mastering, multi-assignment getting to know, version ensembles, and meta-gaining knowledge of algorithms. They have a look at evaluating those meta-getting-to-know strategies on three public clinical photo segmentation datasets: GlaS, okay, and Acropolis. It concludes that the proposed meta-learning strategies yield massive performance improvements compared to traditional transfer learning strategies, particularly for datasets with significant area shifts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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