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[null,"1.","Record Nr.",null,"UNISA996550554403316"]
[null,null,"Autore",null,"Tsapatsoulis Nicolas"]
[null,null,"Titolo",null,"Computer Analysis of Images and Patterns : 20th International Conference, CAIP 2023, Limassol, Cyprus, September 25-28, 2023, Proceedings, Part II"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2023"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"3-031-44240-7"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (295 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.14185"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Keynote Lectures -- Semiconductor Chips in the Center of Geopolitical Competition -- Improving Contour Detection by Surround Suppression of Texture -- Contents - Part II -- Contents - Part I -- Biometrics - Human Pose Estimation - Action Recognition -- A Systematic Approach for Automated Lecture Style Evaluation Using Biometric Features -- 1 Introduction -- 2 Literature Review -- 3 Defining a Good Lecture Style Profile -- 4 Lecture Style Quality Score Estimation. -- 4.1 Facial Expressions -- 4.2 Activity Detection -- 4.3 Speech Recognition -- 4.4 Hand Movement -- 4.5 Facial Pose Estimation -- 4.6 Merging Metrics -- 5 Evaluation -- 6 Conclusions -- References -- Highly Crowd Detection and Counting Based on Curriculum Learning -- 1 Introduction -- 2 Proposed Approach -- 3 Dataset -- 4 Experimental Results -- 5 Conclusion -- References -- Race Bias Analysis of Bona Fide Errors in Face Anti-spoofing -- 1 Introduction -- 2 Background -- 2.1 Face Anti-spoofing -- 2.2 Bias in Machine Learning -- 3 Experimental Setup -- 3.1 The VQ-VAE Classifier -- 3.2 Overview of the Bias Analysis Process -- 4 Bias Analysis on SiW -- 4.1 Statistical Analysis of the Binary Outcomes -- 4.2 Statistical Analysis of the Scalar Responses -- 5 Bias Analysis on RFW -- 6 Conclusion -- References -- Fall Detection with Event-Based Data: A Case Study -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 The Data Set -- 3.2 Fall Detection Approach -- 4 Experimental Results -- 5 Discussion and Conclusion -- References -- Towards Accurate and Efficient Sleep Period Detection Using Wearable Devices -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset -- 3.2 "]
[{"$nulls":4},"Problem Modelling -- 4 Models -- 5 Evaluation and Results -- 5.1 Baseline Study -- 5.2 Machine Learning and Deep Learning Models -- 6 Clinical Results -- 7 Conclusion and Future Work."]
[{"$nulls":4},"References -- RLSTM: A Novel Residual and Recurrent Network for Pedestrian Action Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Spatio-Temporal RLSTM -- 3.2 MapGrad Layer -- 4 Experimental Results -- 4.1 Dataset -- 4.2 Ablation Study -- 4.3 Comparison with the State-of-the-Art -- 5 Conclusions and Future Work -- References -- Biomedical Image and Pattern Analysis -- Temporal Sequences of EEG Covariance Matrices for Automated Sleep Stage Scoring with Attention Mechanisms -- 1 Introduction -- 2 State of the Art -- 3 Method -- 3.1 From EEG Signals to Covariance-Derived SPD Matrices -- 3.2 The Model -- 4 Experiments -- 4.1 Dataset Used -- 4.2 Model Validation -- 4.3 Reproducing the State of the Art -- 4.4 Analysis of Results -- 5 Conclusion -- References -- A Complete AI-Based System for Dietary Assessment and Personalized Insulin Adjustment in Type 1 Diabetes Self-management -- 1 Introduction -- 2 Related Work -- 2.1 Computer Vision in Dietary Assessment -- 2.2 Reinforcement Learning in Blood Glucose Control -- 3 Methodology -- 3.1 System Outline -- 3.2 Computer Vision Module -- 3.3 Reinforcement Learning Module -- 4 Experimental Setup -- 4.1 Food-Related Datasets -- 4.2 In Silico Environment -- 4.3 Scenario -- 5 Results -- 5.1 Complete System -- 5.2 Computer Vision Module -- 6 Conclusion -- References -- COFI - Coarse-Semantic to Fine-Instance Unsupervised Mitochondria Segmentation in EM -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset Description and Annotation -- 3.2 Coarse Semantic Segmentation -- 3.3 Fine Instance Segmentation -- 4 Experiments and Results -- 5 Discussion and Conclusion -- References -- Empirical Study of Attention-Based Models for Automatic Classification of Gastrointestinal Endoscopy Images -- 1 Introduction -- 2 Attention-Based Models -- 2.1 MobileViT Family -- 2.2 CoAtNet -- 2.3 CMT."]
[{"$nulls":4},"2.4 DaViT -- 3 Dataset and Metrics -- 3.1 Hyper-Kvasir Dataset -- 3.2 Performance Metrics -- 4 Experiments and Results -- 4.1 Implementation Details -- 4.2 Comparison of Architectures -- 4.3 Influence of the Green Patches -- 4.4 Comparison to the State of the Art -- 5 Conclusions and Future Work -- References -- Classification of Breast Micro-calcifications as Benign or Malignant Using Subtraction of Temporally Sequential Digital Mammograms and Machine Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Collection and Description -- 2.2 MCs Detection and Segmentation -- 2.3 Feature Extraction and Selection -- 2.4 Training and Comparison of Classifier Designs -- 3 Experimental Results -- 4 Discussion -- 5 Conclusion -- References -- Fourier Descriptor Loss and Polar Coordinate Transformation for Pericardium Segmentation -- 1 Introduction -- 1.1 Related Work -- 1.2 Contribution -- 2 Methodology -- 3 Experiments -- 3.1 Data -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusions and Future Work -- References -- Stroke Risk Stratification Using Transfer Learning on Carotid Ultrasound Images -- 1 Introduction -- 1.1 Artificial Intelligence-Based Stroke Risk Assessment -- 2 Materials and Methods -- 2.1 Carotid Ultrasound Images and Patient Data -- 2.2 Image Preprocessing -- 2.3 Transfer Learning Models -- 2.4 Model Training Process -- 2.5 Model Carotid Plaque Classification Performance -- 2.6 Saliency Maps Per Carotid Plaque Category -- 3 Results -- 4 Discussion -- References -- A Comparative Study of Explainable AI models in the Assessment of Multiple Sclerosis -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Learning Method A -- 3.2 Learning Method B: ArgEML -- 3.3 Evaluation Metrics -- 4 "]
[{"$nulls":4},"Results -- 4.1 Learning Method A -- 4.2 Learning Method B: ArgEML -- 4.3 Evaluation of the Learning Methods -- 5 Discussion."]
[{"$nulls":4},"6 Concluding Remarks -- References -- General Vision - AI Applications -- Biometric Recognition of African Clawed Frogs -- 1 Introduction -- 2 Background and Related Work -- 3 Methodology -- 3.1 Data Acquisition -- 3.2 Pattern Extraction -- 3.3 Contour Delineation -- 4 Experiments -- 4.1 Data Set -- 4.2 Experimental Setup -- 4.3 Results -- 5 Discussion -- 6 Conclusion -- References -- Teacher-Student Synergetic Knowledge Distillation for Detecting Alcohol Consumption in NIR Iris Images -- 1 Introduction -- 2 Related Works -- 3 Research Methodology -- 3.1 Dataset Description -- 3.2 Feature Extractor -- 4 Experimental Analysis -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Conclusion and Future Works -- References -- Performance Assessment of Fine-Tuned Barrier Recognition Models in Varying Conditions -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Data Collection -- 3.2 Model Training -- 4 Experimental Results -- 5 Conclusion -- References -- Keyrtual: A Lightweight Virtual Musical Keyboard Based on RGB-D and Sensors Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Non-wearable Hand-Based Interaction -- 2.2 Natural Virtual Interfaces for Music -- 3 Proposed Method -- 3.1 Preliminary Phase -- 3.2 Real-Time Phase -- 4 Experimental Environment -- 4.1 Experimental Setup -- 4.2 Experiments Execution -- 4.3 Results -- 5 Conclusions -- References -- Classification of Honey Pollens with ImageNet Neural Networks -- 1 Introduction -- 2 Materials and Methods -- 2.1 Ground Truth -- 2.2 ImageNet Networks -- 3 Experimental Work -- 3.1 Results per Types and Multiclass Metrics -- 4 Conclusions and Discussion -- References -- Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space -- 1 Introduction -- 2 Proposed Method -- 2.1 Imposing Linearity onto Latent Space -- 2.2 Evaluation Metrics."]
[{"$nulls":4},"3 Experiments and Results -- 3.1 Dataset -- 3.2 Architecture and Training -- 3.3 Results -- 4 Discussion and Future Work -- 5 Conclusions -- References -- Unsupervised State Representation Learning in Partially Observable Atari Games -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experimental Details -- 5 Results -- 6 Discussion -- 7 Conclusion -- References -- Structural Analysis of the Additive Noise Impact on the -tree -- 1 Introduction -- 2 Hierarchical Representations -- 2.1 The -tree Representation -- 2.2 Persistent Hierarchies -- 3 Noise Impact on the Tree Structure -- 3.1 Study on a Noisy Constant Image -- 3.2 Comparison with Natural Images -- 4 Impact of the Noise on Nodes Persistence -- 5 Conclusion and Perspectives -- References -- Augmented Reality for Indoor Localization and Navigation: The Case of UNIPI AR Experience -- 1 Introduction -- 2 Related Work -- 3 Design and Implementation -- 3.1 Background Technologies -- 3.2 Methodology -- 3.3 System Overview -- 3.4 Implementation -- 4 Results -- 4.1 System in Practice -- 4.2 Experimentation -- 5 Discussion -- 6 Conclusion -- References -- A Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial Images -- 1 Introduction -- 2 Background and Related Work -- 3 Proposed Approach -- 3.1 Dataset for Disaster Recognition Using UAVs -- 3.2 Disaster Recognition Network Architecture -- 3.3 Baseline Designs -- 3.4 Data Pre-processing and Training Process -- 3.5 Explainability Through Grad-CAM -- 4 Experimental Evaluation and Results -- 4.1 Configuration and Evaluation Metrics -- 4.2 Disaster Classification Evaluation -- 4.3 Gram-CAM Evaluation -- 5 Conclusion and Future Work -- References -- Perceptual Light Field Image Coding with CTU Level Bit Allocation -- 1 Introduction -- 2 The Proposed Method."]
[{"$nulls":4},"2.1 Designed CTU Level Bit Allocation Strategy with Perceptual Consistency."]