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检索条件"任意字段=10th International Conference on Computer Analysis of Images and Patterns"

共 4145 条记录，以下是1-10 订阅

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20th international conference on computer analysis of images and patterns, CAIP 2023

20th International Conference on Computer Analysis of Images...

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20th international conference on computer analysis of images and patterns, CAIP 2023

ISBN: (纸本)9783031442360

the proceedings contain 54 papers. the special focus in this conference is on international conference on computer analysis of images and patterns. the topics include: Empirical Study of Attention-Based Models for Automatic Classification of Gastrointestinal Endoscopy images;classification of Breast Micro-calcifications as Benign or Malignant Using Subtraction of Temporally Sequential Digital Mammograms and Machine Learning;fourier Descriptor Loss and Polar Coordinate Transformation for Pericardium Segmentation;stroke Risk Stratification Using Transfer Learning on Carotid Ultrasound images;A Comparative Study of Explainable AI models in the Assessment of Multiple Sclerosis;biometric Recognition of African Clawed Frogs;Teacher-Student Synergetic Knowledge Distillation for Detecting Alcohol Consumption in NIR Iris images;performance Assessment of Fine-Tuned Barrier Recognition Models in Varying Conditions;Keyrtual: A Lightweight Virtual Musical Keyboard Based on RGB-D and Sensors Fusion;classification of Honey Pollens with ImageNet Neural Networks;highly Crowd Detection and Counting Based on Curriculum Learning;defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space;unsupervised State Representation Learning in Partially Observable Atari Games;structural analysis of the Additive Noise Impact on the α-tree;Augmented Reality for Indoor Localization and Navigation: the Case of UNIPI AR Experience;a Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial images;Perceptual Light Field Image Coding with CTU Level Bit Allocation;a Comparative Performance Assessment of Different Video Codecs;race Bias analysis of Bona Fide Errors in Face Anti-spoofing;fall Detection with Event-Based Data: A Case Study;towards Accurate and Efficient Sleep Period Detection Using Wearable Devices;RLSTM: A Novel Residual and Recurrent Network for Pedestrian Action Classification;Temporal Sequences of EEG Covariance

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20th international conference on computer analysis of images and patterns, CAIP 2023

20th International Conference on Computer Analysis of Images...

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20th international conference on computer analysis of images and patterns, CAIP 2023

ISBN: (纸本)9783031442391

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19th international conference on computer analysis of images and patterns, CAIP 2021

19th International Conference on Computer Analysis of Images...

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19th international conference on computer analysis of images and patterns, CAIP 2021

ISBN: (纸本)9783030891305

the proceedings contain 87 papers. the special focus in this conference is on computer analysis of images and patterns. the topics include: Preface;simultaneous Bi-directional Structured Light Encoding for Practical Uncalibrated Profilometry;HPA-Net: Hierarchical and Parallel Aggregation Network for Context Learning in Stereo Matching;MTStereo 2.0: Accurate Stereo Depth Estimation via Max-Tree Matching;H-OCS: A Hybrid Optic Cup Segmentation of Retinal images;retinal Vessel Segmentation Using Blending-Based Conditional Generative Adversarial Networks;U-Shaped Densely Connected Convolutions for Left Ventricle Segmentation from CMR images;deep Learning Approaches for Head and Operculum Segmentation in Zebrafish Microscopy images;shape analysis Approach Towards Assessment of Cleft Lip Repair Outcome;MMEC: Multi-Modal Ensemble Classifier for Protein Secondary Structure Prediction;patch-Level Nuclear Pleomorphism Scoring Using Convolutional Neural Networks;automatic Myelofibrosis Grading from Silver-Stained images;Joint Global ICP for Improved Automatic Alignment of Full Turn Object Scans;a Deep Learning-Based Pipeline for Celiac Disease Diagnosis Using Histopathological images;HEp-2 Cell Image Recognition with Transferable Cross-Dataset Synthetic Samples;clinically Guided Trainable Soft Attention for Early Detection of Oral Cancer;small and Large Bile Ducts Intrahepatic Cholangiocarcinoma Classification: A Preliminary Feature-Based Study;A Review on Breast Cancer Brain Metastasis: Automated MRI Image analysis for the Prediction of Primary Cancer Using Radiomics;An Adaptive Semi-automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network;a three-Dimensional Reconstruction Integrated System for Brain Multiple Sclerosis Lesions;Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings;progressive Contextual Excitation for Smart Farming Application.

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19th international conference on computer analysis of images and patterns, CAIP 2021

19th International Conference on Computer Analysis of Images...

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19th international conference on computer analysis of images and patterns, CAIP 2021

ISBN: (纸本)9783030891275

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Dental Anomalies Detection Based on the analysis of Reconstructed Intraoral Scan images 10

Dental Anomalies Detection Based on the Analysis of Reconstr...

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10th international conference on Information Technology and Nanotechnology, ITNT 2024

作者： Pchelkina, Yuliya Paringer, Rustam Savelyeva, Polina Samara National Research University Samara Russia Samara National Research University Ipsi Ras - Branch Of The Fsrc 'Crystallography And Photonics' Samara Russia Moscow Regional Clinical Research Institute Named After M.F. Vladimirsky Moscow Russia

ISBN: (纸本)9798350372939

A method for automated detecting dental system anomalies by identifying the dentition areas and comparing patterns on the reconstructed medical data of oral objects' models is proposed. the algorithm includes the stages of segmentation and identification of dentition areas using computer vision methods and is a unified method that that does not depend on the scanner model. © 2024 IEEE.

关键词： Image analysis

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Machine learning-based automatic implant size prediction from CT images in total knee arthroplasty 10

Machine learning-based automatic implant size prediction fro...

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10th IEEE RAS/EMBS international conference for Biomedical Robotics and Biomechatronics (BioRob)

作者： Katragadda, Sandeep de Souza, Kevin Stryker UK Digital Robot & Enabling Technol Div Newbury Berks England

ISBN: (纸本)9798350386523;9798350386530

Prediction of suitable implant sizes from CT images of a joint anatomy can be achieved using templating methods. Automatic templating (that requires no manual intervention) is useful for speeding up computer or robot-assisted surgical plan generation. In our previous work, the automatic templating task in total knee arthroplasty is achieved by automatic bone segmentation followed by matching a set of anatomical landmarks with the corresponding points on the candidate implants (of different sizes). this paper improves the approach by adding a linear regression module to the framework and thereby increases the prediction accuracy significantly without increasing the processing time. Experimental analysis on 292 knee CT images proved that the proposed approach predicts the implant sizes in more than 95% of femur cases and in more than 98% of tibia cases with at most 1-size difference from the sizes used by an experienced surgeon.

关键词： Arthroplasty

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Predicting Asthma Attacks through AI-Powered thermal Imaging analysis of Breathing patterns 10

Predicting Asthma Attacks Through AI-Powered Thermal Imaging...

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IEEE 10th international conference on Big Data Computing Service and Machine Learning Applications (IEEE BigDataService)

作者： Hamza, Amir Himeur, Yassine Amira, Abbes Oulefki, Adel Univ Jijel Dept Elect Nondestruct Lab Jijel Algeria Univ Dubai Coll Engn & Informat Technol Dubai U Arab Emirates Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates De Montfort Univ Inst Artificial Intelligence Leicester Leics England

ISBN: (纸本)9798350366396;9798350366389

Breathing patterns understanding plays a crucial role in assessing human health, notably in the early detection and prediction of asthma attacks. Identifying these patterns, including inhalation and exhalation, is essential for doctors. However, the current methods, such as invasive techniques like belts or nasal probes, often cause considerable discomfort to patients. Given the rapid advancements in artificial intelligence (AI) and computer vision, there is a pressing need to leverage these technologies for developing non-invasive diagnostic solutions. Leveraging the precision of AI, this study introduces an innovative asthma attack prediction model employing contactless thermal imaging to evaluate asthma severity. By meticulously analyzing thermal patterns emanating from the nasal area, our model promises a groundbreaking shift in asthma diagnostics. Validation on a robust benchmark dataset collected at the University of Technology Malaysia showcased exceptional performance metrics, with accuracy, precision, recall, and F1-score each reaching an unprecedented 99.49%, 98.01%, 98.4%, and 99.2% respectively. Beyond its high accuracy, this method emphasizes patient comfort and accessibility, potentially democratizing asthma management across varied healthcare settings. this research not only underscores the feasibility of non-invasive asthma assessment but also sets a precedent for future explorations in leveraging computer vision for respiratory condition diagnostics.

关键词： Asthma attack prediction Transfer learning thermal images VGG16 Inhale Exhale

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Pediatric Pneumonia Detection in Chest X-ray images: A Deep Feature analysis Approach Enhanced with LightGBM 10

Pediatric Pneumonia Detection in Chest X-ray Images: A Deep ...

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10th IEEE international conference on Electronics, Computing and Communication Technologies (IEEE CONECCT)

作者： Godbin, A. Beena Jasmine, S. Graceline Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9798350385939;9798350385922

Pediatric pneumonia remains a significant concern for public health, underscoring the need for efficient and precise diagnostic tools to enable timely intervention. In this research, we introduce a novel approach for detecting pediatric pneumonia in chest X-ray images. Our method leverages deep features extracted from DenseNet169, MobileNet, and EfficientNetB0 Neural Networks, which capture intricate patterns and textures indicative of pneumonia presence. these extracted features are then inputted into LightGBM, enabling effective learning of complex data patterns and relationships for classification purposes. We evaluated the effectiveness of our technique using a dataset comprising 5856 chest X-ray images from patients. Our approach achieved a remarkable accuracy of 96.16% with EfficientNet in binary classification (Pneumonia, Normal). Our method boasts several advantages, including robustness to variations in image quality, scalability, and interpretability. We evaluated our model's performance based on metrics such as accuracy, sensitivity, precision, specificity, and F1-score, highlighting the potential of combining deep learning and gradient boosting techniques for pediatric pneumonia detection. this paves the way for improved clinical decision support systems in pediatric healthcare settings.

关键词： Pneumonia Machine Learning Deep Learning CNN Transfer Learning Chest X-Rays

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Sensor-Based Gait analysis and Machine Learning for Predictive Fall Risk Assessment in Adults 10

Sensor-Based Gait Analysis and Machine Learning for Predicti...

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10th international conference on Biosignals, images and Instrumentation, ICBSII 2024

作者： Gayathri, T. Sathivel, Boopathi Indiera, M. Sanjay Kumar, C. Pravin Kumar, S. SudalaiMani, B. Kpr Institute of Engineering and Technology Department of Biomedical Engineering Coimbatore India Prashan Medical Technologies Coimbatore India

ISBN: (纸本)9798350350951

the need for fall prediction and prevention measures arose from the increasing global ageing population's increased incidence of falls among the elderly. the combination of Internet of things (IoT) devices and machine learning algorithms is becoming more and more prevalent in these methods. these algorithms are able to identify changes in gait patterns based on data gathered from many sensors, such as accelerometers and gyroscopes, providing early warning signs of approaching falls. © 2024 IEEE.

关键词： Internet of things

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CHARACTERIZING RESIDENTIAL BUILDING patterns IN HIGH-DENSITY CITIES USING GRAPH CONVOLUTIONAL NEURAL NETWORKS 29th

CHARACTERIZING RESIDENTIAL BUILDING PATTERNS IN HIGH-DENSITY...

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29th international conference of the Association-for-computer-Aided-Architectural-Design-Research-in-Asia (CAADRIA)

作者： Jia, Muxin Zhang, Kaiheng Narahara, Taro New Jersey Inst Technol Newark NJ 07102 USA

ISBN: (纸本)9789887891826

In urban morphology studies, accurately classifying residential building patterns is crucial for informed zoning and urban design guidelines. While machine learning, particularly neural networks, has been widely applied to urban form taxonomy, most studies focus on grid-like data from street-view images or satellite imagery. Our paper provides a novel framework for graph classification by extracting features of clustering buildings at different scales and training a spectral-based GCN model on graph-structured data. Furthermore, from the perspective of urban designers, we put forward corresponding design strategies for different building patterns through data visualization and scenario analysis. the findings indicate that GCN has a good performance and generalization ability in identifying residential building patterns, and this framework can aid urban designers or planners in decision-making for diverse urban environments in Asia.

关键词： Urban morphology Machine learning Building pattern classification Graph convolutional neural networks

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