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Research on depth estimation and human body reconstruction effects in single-image scene reconstruction 2

Research on depth estimation and human body reconstruction e...

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhou, Xiuyuan Li, Yuanzhen Zhu, Yaling Liu, Ruilin Yang, Lina Jing, Yongxia Lanzhou Institute of Technology Gansu Lanzhou730030 China Yunnan University Yunnan Kunming650091 China Qiongtai Normal University Hainan Haikou571100 China

ISBN: (纸本)9781510689053

With the rapid development of digital technology and deep learning, recovering 3D scene information and reconstructing human bodies from a single image has become a focal point of research in computer vision and computer graphics. This technology has also found widespread application in fields such as cultural relic restoration, autonomous driving, virtual reality, and medical image analysis. In this paper, we explore the challenges posed by the network's contextual perception abilities and the influence of loss functions, which can lead to issues like incomplete depth structures, depth drift, and texture copying. To overcome these obstacles, we propose refined methods that produce highly accurate and structurally sound depth estimates, effectively resolving problems such as texture copying and depth drift. Our methods demonstrate strong generalization capabilities in human depth estimation models, enabling precise depth estimation across various scenarios. © 2025 SPIE.

关键词： Virtual reality

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Prostate medical image segmentation based on federal learning DUnet network 24

Prostate medical image segmentation based on federal learnin...

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2nd International conference on Electronics, Computers and Communication Technology, CECCT 2024

作者： Wang, Hongjuan Guo, Quanbing An, Xinjun Wang, Nan Li, Longfei Sun, Qihang Zhao, Fangzheng Shandong University of Science and Technology Shandong Taian China

ISBN: (纸本)9798400710193

In the medical field, image segmentation is critical for disease diagnosis and treatment. To address challenges such as data heterogeneity and privacy concerns, this paper proposes a federated learning-based DUnet approach for prostate medical image segmentation. This method employs a hybrid architecture of Densenet and Unet, utilizing Densenet on local devices for preliminary training to extract multi-scale features from the ***, the introduction of the Unet structure and hybrid loss function enhances feature richness and balances the characteristics of diverse data, improving the accuracy of image segmentation. Finally, federated learning is employed to aggregate local models into a global model, ensuring data privacy and addressing *** results demonstrate that this method, utilizing the publicly available prostate dataset for prostate medical image segmentation, achieves Accuracy, Recall, and mean pixel accuracy (mPA) metrics of 96.4%, 98.4%, and 98.6%, respectively, across different *** research provides a promising avenue for enhancing prostate medical image segmentation while ensuring data privacy and security. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Enhancing image quality for single-pixel imaging with a score-based diffusion model 2

Enhancing image quality for single-pixel imaging with a scor...

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2nd International Academic conference on Optics and Photonics, IACOP 2024

作者： Tang, Ziyi Yan, Qiurong Li, Yi Yan, Jinwei School of Information Engineering Nanchang University Nanchang330031 China Ji luan Academy Nanchang University Nanchang330031 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

Reconstructing high-quality images at low measurement rates remains a problem in single-pixel imaging. Traditional approaches, including methods based on compressed sensing and end-to-end deep learning models, often fail to deliver satisfactory results, especially under low sampling rates. These methods generally exhibit inherent limitations when dealing with undersampling data, resulting in poor image reconstruction quality. To address this issue, we propose a novel approach using the score-based diffusion model. By leveraging the strong and stable generative capabilities of the diffusion model, we fit the underlying distribution of target images. During the reverse diffusion sampling process, we impose conditional constraints using the undersampling measurements, enabling high-quality image reconstruction. The simulation experiments show that our method outperforms previous methods in terms of PSNR or SSIM at different sampling rates, demonstrating superior reconstruction quality. © 2025 SPIE.

关键词： Pixels

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2nd International Workshop on Medical Optical Imaging and Virtual Microscopy image Analysis, MOVI 2024, held in conjunction with 26th International conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2024

2nd International Workshop on Medical Optical Imaging and Vi...

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2nd International Workshop on Medical Optical Imaging and Virtual Microscopy image Analysis, MOVI 2024, held in conjunction with 26th International conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031777851

The proceedings contain 21 papers. The special focus in this conference is on Medical Optical Imaging and Virtual Microscopy image Analysis. The topics include: Deep Learning for Classifying Anti-Shigella Opsono- Phagocytosis-Promoting Monoclonal Antibodies;multi-target Stain Normalization for Histology Slides;intensity Inhomogeneity Correction for Large Panoramic Electron Microscopy images;Fully Automated CTC Detection, Segmentation and Classification for Multi-channel IF Imaging;lymphoid Infiltration Assessment of the Tumor Margins in H&E Slides;TRP-Net: Transformer with RMM and PPM for High-Efficiency Circulating Abnormal Cells Detection in Multichannel Fluorescence Imaging;color Flow Imaging Microscopy Improves Identification of Stress Sources of Protein Aggregates in Biopharmaceuticals;Learned image Compression for HE-Stained Histopathological images via Stain Deconvolution;CLSMI2T3: 3D CLSM Vasculature Volume reconstruction from A Single 2D Slice by Off-Focal Plane Signal Using Synthetic data;Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging;MDSN: Multi-stage Context-Aware Nuclei Detection-Segmentation Network;structured Model Pruning for Efficient Inference in Computational Pathology;Histopathology image Embedding Based on Foundation Models Features Aggregation for DLBCL Patient Treatment Response Prediction;EM-Compressor: Electron Microscopy image Compression in Connectomics with Variational Autoencoders;AC-UNet: A Self-adaptive Cropping Approach for Kidney Pathology image Segmentation;SAM-Glomeruli: Enhanced Segment Anything Model for Precise Glomeruli Segmentation;A Robust Deep Learning Method for WSI-Level Diseased Glomeruli Segmentation;ensembled SegNeXt Based Glomeruli Segmentation;glomeruli Segmentation in Whole-Slide images: Is Better Local Performance Always Better?.

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Multi-level Enhanced Copy-Paste with Confidence-Based Semi-Supervised Medical image Segmentation Method 24

Multi-level Enhanced Copy-Paste with Confidence-Based Semi-S...

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2nd International conference on Electronics, Computers and Communication Technology, CECCT 2024

作者： Liu, Hanqiang Guo, Xiangkai Zhao, Feng Institute of School Computer Science Shaanxi Normal University Shaanxi Xi’an China Institute of School of Communications and Information Engineering School of Artificial Intelligence Xian University of Posts and Telecommunications Shaanxi Xian China

ISBN: (纸本)9798400710193

In the field of semi-supervised medical image segmentation, we often face three main challenges. Firstly, there’s an issue with the empirical distribution not quite matching between labeled and unlabeled images. If we treat these two types of data separately, it can result in substantial loss of knowledge acquired from the labeled data. Secondly, existing methods haven’t fully harnessed the potential of labeled data. Thirdly, when using pseudo-labels for training, low-quality pseudo-labels may contain noise. The noise in the labels can lead the model to learn incorrect information, thereby affecting the model’s generalization ability. Regarding the above issues, we propose a Multi-level Enhanced Copy-Paste with Confidence-Based Semi-Supervised Medical image Segmentation Method. Firstly, our method encourages the model to learn the average features of the data from both labeled and unlabeled data. Secondly, we propose a Multi-level Enhanced Copy-paste (MECP) method to better utilize both labeled data and unlabeled data. For the augmented data, combining a multi-backpropagation training strategy significantly improves the quality of the pseudo-labels. Thirdly, to handle potentially noisy pseudo-labels, we’ve designed a pseudo-label confidence loss function. Our method significantly enhances segmentation performance by effectively integrating labeled and unlabeled data, improving pseudo-label quality, and reducing noise, making it particularly valuable in scenarios with limited labeled data. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Remote sensing image super-resolution reconstruction algorithm based on improved anchored neighborhood regression 2

Remote sensing image super-resolution reconstruction algorit...

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Ge, Wei Fan, Caixia Chongqing Research Institute Changchun University of Science and Technology Chongqing401135 China Changchun University of Science and Technology Jilin Changchun130022 China

ISBN: (纸本)9781510689053

In recent years, the application scenarios of satellite remote sensing images have become increasingly widespread. However, due to limited acquisition equipment and cost constraints, the images obtained by satellite sensors usually don't have sufficient resolution to meet the analysis and discrimination research of subsequent satellite images. Based on the ideas of neighbor embedding and sparse representation, the super-resolution reconstruction method of remote sensing images using Adjusted anchored neighborhood regression (A+) is studied in this paper. This algorithm is based on the sparse representation theory to train a dictionary. With the dictionary atoms as the center, K feature patches that are most similar to them are selected to form a neighborhood, and then the projection matrix corresponding to this neighborhood is calculated. When reconstructing, the atom that is most similar to the low resolution feature patch is selected, and the projection matrix corresponding to this atom is used to obtain the reconstructed high-resolution image. This article compares A+ with other methods based on sparse representation through simulation experiments, verifying the effectiveness of the algorithm. © 2025 SPIE.

关键词： Satellite imagery

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data, Information and Computing Science - Proceedings of the 2nd International conference, CDICS 2024

Data, Information and Computing Science - Proceedings of the...

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2nd International conference on data, Information and Computing Science, CDICS 2024

ISBN: (纸本)9781643685762

The proceedings contain 10 papers. The topics discussed include: comparative analysis of deep learning methods for automated diagnosis of pulmonary diseases from chest X-ray images: a study based on ICD-10;deep residual learning for fruits of ceremonial plants recognition;PiVisionSort: integrating image processing and machine learning for material recognition on conveyor belts;intelligent detection of potholes using SSD algorithm and auto-alert notification system for user;the use of motion and gaze features to detect speaking intention in VR-mediated communication environments;optimized retinal vessel segmentation using IS-Net and high-resolution dataset;a framework for adopting machine learning in the clinical domain;and a study on a domain BERT-based named entity recognition method for faulty text.

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Multi-classifier semi-supervised data stream classification algorithm based on online learning 24

Multi-classifier semi-supervised data stream classification ...

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2nd International conference on Electronics, Computers and Communication Technology, CECCT 2024

作者： Liu, Changjie Lin, Xianning Guangdong Technology College Guangdong Zhaoqing China

ISBN: (纸本)9798400710193

Massive data is continuously generated from a variety of real-world scenarios and fields in all walks of life, including data recorded in hardware sensors, huge amounts of text, voice and image data, various user data generated by commonly used systems and software. The common denominator of these data is continuous generation, large volume, and lack of correct and reliable class labels. In the research progress of data stream classification algorithms, there are relatively mature solutions to solve the problem of rapid data generation and large volume, but how to accurately classify data instances when class labels are delayed or directly missing is still a problem worth studying. To handle these challenges, a multi-classifier semi-supervised data stream classification algorithm based on continuous learning is proposed. The classifier adopts an ensemble pool framework and uses the principle of majority voting to label the newly arrived instances. After the instances get a small number of labels, the current base classifier is updated by calculating the forgetting measurement index, and a large number of unlabeled instances are utilized to participate in the continuous learning process of the base classifier. Evaluations on benchmark datasets shows the advantages of the algorithm. In the case of using a relatively simple ensemble model, a simple update strategy and implicit concept drift adaptation are adopted, and the classification accuracy of the algorithm on the data stream can be ahead of the existing algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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2nd International conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

2nd International Conference on Security and Information Tec...

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2nd International conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

ISBN: (纸本)9789819777853

The proceedings contain 46 papers. The special focus in this conference is on Security and Information Technologies with AI. The topics include: Authentication of image Fidelity in Watermarking-QR-Code Approving Copyright Systems;a Zero-Watermarking image Scheme in Normalized Cross-Correlation with Robust Copyright Protections;a Novel Approach for Detecting and Analyzing Cyber-Attacks in Cyber-Physical Systems;white-Box Penetration Testing for Hash Collision Attack on Web Applications;towards Zero Trust for Financial Sectors: A Proposed Framework on Trust Evaluation;FiT-DPKI: Decentralized Public Key Infrastructure with Flexibility and Transparency for IoT Networks;Revolutionizing Healthcare: Case Studies of AI Algorithms Transforming the Field of Medicine;analysis and Detection of Abnormal Transactions on Ethereum;feasibility Analysis Study on Constructing a Grid Intrusion Detection System Using Semi-supervised Learning Models;Securing NLP Systems: A Comprehensive AI-Based Approach;A New Scheme Modeling Gym Membership Transactions with NFT Systems;A Text to Human-Like Speech Using Tacotron-Based TTS Model;A Novel Malware Classification Using CNN-SVM Deep Learning Method Based on Transfer Learning Architecture;enhancing Project Programming Hour Prediction with Regression Analysis Techniques—A Case Study of Company D;a New Traceable One-Time Address Scheme Secure Against Privilege Escalation Attack;An Object-Based Multi-level Authentication Framework for AR e-Book;a Door Lock System Based on Visual Cryptography;Automatic APT Attack reconstruction Supporting Lateral Movement;using Open-Source Intelligence to Archive Criminal Organizations;FIDO-Based Access Control Mechanism in Named data Networking;empirical Approach to a Fine-Tuning Using Forgetting in Large Language Models;automatic Wound Segmentation with Deep Convolutional Neural Networks;Prediction of the Prevalence of COVID-19 Using Epidemic Differential Equations and Deep Learning Network.

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Detection of Potato Leaf Diseases Using PSOK image Segmentation and CNN Model 2nd

Detection of Potato Leaf Diseases Using PSOK Image Segmentat...

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2nd International conference on Agriculture-Centric Computation

作者： Barui, Partho Bag, Soumen Das, Ayan Indian Inst Technol ISM Dhanbad Dept Comp Sci & Engn Dhanbad 826004 Bihar India

ISBN: (纸本)9783031744396;9783031744402

Potatoes are one of the most cultivated and consumed crops worldwide. Disease control in potato plants is crucial for efficient production of potatoes. In this paper, we propose a method to detect and classify diseases from the images of potato leaves. For this, we propose a deep neural architecture to classify the plant diseases from leaf images. We have used the PSOK image segmentation method to segment the leaf images into meaningful features. data augmentation techniques like flipping, rotating, skewing, and zooming are used to increase the dataset. We compare the performance of our proposed model with pretrained model. We show that our proposed architecture combined with XGBoost trained on the augmented dataset achieves an accuracy of 97.81%, outperforming the existing methods.

关键词： image segmentation

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