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[null,null,"Autore",null,"Christensen H. I (Henrik I.), <1962->"]
[null,null,"Titolo",null,"Computer Vision Systems : 14th International Conference, ICVS 2023, Vienna, Austria, September 27-29, 2023, Proceedings"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2023"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"9783031441370"]
[{"$nulls":4},"3031441370"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (466 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.14253"]
[null,null,"Disciplina",null,"006.37"]
[null,null,"Soggetti",null,"Computer vision"]
[{"$nulls":4},"Robotics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- Humans and Hands -- Tracking and Identification of Ice Hockey Players -- 1 Introduction -- 2 Related Work -- 2.1 Dataset -- 2.2 Player Detection -- 2.3 Player Tracking -- 2.4 Number Recognition -- 3 System Overview -- 3.1 Player Detection -- 3.2 Player Tracking -- 3.3 Player Identification -- 4 Results -- 4.1 Dataset -- 4.2 Player Tracking -- 4.3 Player Identification -- 5 Conclusion -- References -- Dedicated Encoding-Streams Based Spatio-Temporal Framework for Dynamic Person-Independent Facial Expression Recognition -- 1 Introduction -- 2 Related Works -- 3 Proposed Deep CNN-LSTM for Dynamic FER -- 4 Experimental Analysis -- 4.1 Dynamic FER Datasets -- 4.2 Evaluation of the Proposed CNN-LSTM -- 4.3 Comparison Against State-of-the-art -- 4.4 Confusion Matrix-Based Analysis -- 4.5 Ablation Study -- 4.6 Implementation Details and Running Time -- 5 Conclusion -- References -- Hands, Objects, Action! Egocentric 2D Hand-Based Action Recognition -- 1 Introduction -- 2 Related Work -- 3 Hand-Based 2D Action Recognition -- 3.1 Object Detection and Its Position -- 3.2 Hand Pose Estimation -- 3.3 Action Recognition -- 4 Evaluation -- 4.1 Learning Procedure -- 4.2 Stage I - Preliminary Evaluation -- 4.3 Stage II -Evaluation of a Complete Pipeline -- 4.4 Ablation Study -- 5 Conclusion -- References -- WiFi CSI-Based Long-Range Through-Wall Human Activity Recognition with the ESP32 -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 3.1 Hardware -- 3.2 "]
[{"$nulls":4},"Recording Environment -- 3.3 Data Collection and Pre-processing -- 4 Evaluation -- 4.1 Model Training -- 4.2 Presence Detection Results -- 4.3 Activity Recognition Results -- 5 Conclusion -- References -- PseudoDepth-SLR: Generating Depth Data for Sign Language Recognition -- 1 Introduction -- 2 Related Work -- 3 Methodology."]
[{"$nulls":4},"3.1 Proposed Architecture -- 3.2 Pseudo Depth Data Generation -- 4 Experimental Details -- 4.1 Dataset and Evaluation -- 4.2 Implementation Details -- 5 Results and Analysis -- 5.1 How Significant is Depth Data? -- 5.2 Comparison with State-of-the-Art Results -- 6 Ablation Study -- 6.1 Depth Flow Data -- 6.2 Pseudo Depth Data Generation -- 7 Conclusion -- References -- Slovo: Russian Sign Language Dataset -- 1 Introduction -- 2 Related Work -- 2.1 Sign Language Datasets in Russian Domain -- 2.2 Others Sign Language Datasets -- 2.3 Sign Language Dataset Collection -- 3 Dataset Creation -- 4 Dataset Description -- 5 Experiments -- 6 Conclusion -- References -- Non-contact Heart Rate Monitoring: A Comparative Study of Computer Vision and Radar Approaches -- 1 Introduction -- 2 Non-contact Heart Rate Monitoring -- 2.1 CV-Based HR Monitoring -- 2.2 Radar-Based HR Monitoring -- 3 CV and Radar-Based DMS Testbench Architectures -- 3.1 Proposed CV-Based HR Monitoring Architecture -- 3.2 Proposed Radar-Based HR Monitoring Architecture -- 4 Experiment Design and Results Analysis -- 4.1 Performance Validation -- 4.2 Variation of IBI Distribution with Sensor Modality -- 4.3 Impact of Distance on CV and Radar-Based HR Detection -- 4.4 Impact of Illumination on CV-Based HR Detection -- 4.5 Impact of Motion on CV and Radar-Based HR Detection -- 5 Discussion and Conclusions -- References -- Medical and Health Care -- CFAB: An Online Data Augmentation to Alleviate the Spuriousness of Classification on Medical Ultrasound Images -- 1 Introduction -- 2 Approach -- 2.1 Weakly Supervised Lesion Localization -- 2.2 Mixed Samples Generation -- 2.3 Collaborative Training Framework -- 3 Experiences -- 3.1 Implementation Details -- 3.2 Performance Comparison -- 3.3 Effectiveness of Lesion Localization -- 3.4 Ablation Studies -- 3.5 Visualization -- 4 Conclusion -- References."]
[{"$nulls":4},"Towards an Unsupervised GrowCut Algorithm for Mammography Segmentation -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experimental Results -- 5 Conclusions and Future Work -- References -- DeepLabV3+ Ensemble for Diagnosis of Cardiac Transplant Rejection -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Dataset -- 3.2 Data Preprocessing -- 3.3 DeepLabV3+ -- 3.4 Stacked U-Net -- 3.5 mU-Net++ -- 3.6 Post-processing -- 4 Results -- 4.1 Models Evaluation -- 5 Conclusions and Discussion -- References -- Farming and Forestry -- Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and Synthetic Prior -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experiments -- 5 Discussion and Conclusion -- References -- SIFT-Guided Saliency-Based Augmentation for Weed Detection in Grassland Images: Fusing Classic Computer Vision with Deep Learning -- 1 Introduction -- 2 Related Work -- 2.1 Rumex Detection -- 2.2 Augmentation in Deep Learning -- 3 Method -- 3.1 YOLOR -- 3.2 SIFT-Guided Saliency-Based Augmentation Module -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Metrics -- 4.3 Fixed Training Settings -- 5 Experiments -- 5.1 Baseline Performance -- 5.2 Ablation Study of Module Usage Probability psift -- 5.3 Ablation Study of Overlay Ratio rsift -- 6 Conclusion -- References -- Key Point-Based Orientation Estimation of Strawberries for Robotic Fruit Picking -- 1 Introduction -- 2 Related Work -- 3 The Approach -- 3.1 Key Point Detection -- 3.2 Key Point-Based Orientation -- 3.3 Improved Estimation of the Pitch Angle -- 4 Evaluation Setup -- 5 "]
[{"$nulls":4},"Results -- 6 Conclusions and Future Work -- References -- Residual Cascade CNN for Detection of Spatially Relevant Objects in Agriculture: The Grape-Stem Paradigm -- 1 Introduction -- 2 Related Work -- 2.1 Instance Segmentation -- 2.2 Object Detection in Agriculture -- 3 Motivation."]
[{"$nulls":4},"4 Method -- 4.1 Data Collection/Annotation -- 4.2 Yolo-V5 Architecture -- 4.3 Residual Connection of Detectors -- 5 Experimental Results -- 5.1 Evaluation -- 5.2 Conclusions -- References -- Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Preprocessing -- 3.2 Neural Network Architectures Without Recurrent Connections -- 3.3 Neural Network Architectures with Recurrent Connections -- 3.4 Evaluation Metrics -- 3.5 Other Experimental Hyperparameters -- 4 Results -- 5 Discussion -- References -- Automation and Manufacturing -- Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing -- 1 Introduction -- 2 Related Work -- 3 Semi-Siamese Defect Detection Model -- 3.1 Transfer Learning from U-Net Models -- 3.2 Semi-Siamese Network Architecture -- 3.3 Training Objective -- 4 Experiments -- 4.1 Full Dataset Generation Using Data Augmentation -- 5 Results -- 6 Conclusions -- References -- Spatial Resolution Metric for Optimal Viewpoints Generation in Visual Inspection Planning -- 1 Introduction -- 2 Viewpoint Planning Problem Formulation -- 3 State of the Art of the VPP -- 4 Sampling Density Matrix -- 5 Results and Discussions -- 6 Conclusions and Outlook -- References -- A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images -- 1 Introduction -- 1.1 Visual Inspection -- 1.2 Related Work -- 2 Methodology -- 2.1 Data Collection &amp -- Preparation -- 2.2 Network Architecture -- 2.3 Evaluation Protocol -- 2.4 Experimental Results -- 2.5 Patch Detection on Different Pavement Conditions -- 3 Conclusion -- References -- A Flexible Approach to PCB Characterization for Recycling -- 1 Introduction -- 2 Approach -- 2.1 Dataset -- 3 Implementation."]
[{"$nulls":4},"3.1 Segmentation -- 3.2 Components Identification -- 3.3 Rule-Based Classification -- 3.4 ML Classifier -- 4 Results -- 4.1 Rule-Based vs ML Classifier -- 4.2 PCB Classification -- 5 Conclusion -- References -- SynthRetailProduct3D (SyRePro3D): A Pipeline for Synthesis of 3D Retail Product Models with Domain Specific Details Based on Package Class Templates -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Product Package Base Template Model -- 3.2 Object 3D Shape Augmentation -- 3.3 Generative Models for Domain-Specific Add-Ons -- 3.4 Base Texturing -- 3.5 Placing of Domain-Specific Add-Ons -- 4 Results -- 5 Conclusions -- References -- Small, but Important: Traffic Light Proposals for Detecting Small Traffic Lights and Beyond -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Traffic Light Proposal Generator -- 3.2 Traffic Light Detection Module -- 3.3 Training -- 4 Experiments -- 4.1 Quantitative Results -- 4.2 Qualitative Results -- 4.3 Ablation Studies -- 5 Conclusion -- References -- MATWI: A Multimodal Automatic Tool Wear Inspection Dataset and Baseline Algorithms -- 1 Introduction -- 2 Related Work -- 2.1 Datasets and Methods with Sensor Data -- 2.2 Datasets and Methods with Image Data -- 3 MATWI Dataset -- 3.1 Dataset Acquisition -- 3.2 Data Collection Conditions -- 3.3 Hardware Setup -- 3.4 Collected Data -- 4 Algorithms for Wear Estimation -- 4.1 Dataset Training/test Split -- 4.2 Regression Baseline -- 4.3 Histogram-Loss Based Embedding Learning -- 4.4 Quantitative Results -- 5 Conclusions -- 6 Future Work -- References -- Mixing Domains for "]
[{"$nulls":4},"Smartly Picking and Using Limited Datasets in Industrial Object Detection -- 1 Introduction -- 2 Related Work -- 3 Multi-domain Hybrid Datasets -- 3.1 Real Data Acquisition -- 3.2 Synthetic Data Generation -- 4 Experimentation and Results -- 4.1 Experimentation Setup."]
[{"$nulls":4},"4.2 Obtained Results and Discussion."]
[null,null,null,"Sommario/riassunto","The book is a collection of proceedings from the 14th International Conference on Computer Vision Systems (ICVS 2023), held in Vienna, Austria. It focuses on the latest advancements and challenges in computer vision systems, particularly in real-world applications. The conference attracted contributions from researchers across 21 countries, with papers covering topics such as robotics, agriculture, medicine, and industry. Key discussions included addressing the limitations of artificial vision systems in adapting to novel environments. The volume organizes papers into sections like Humans and Hands, Medical and Health Care, and Automation and Mobile Robotics. Invited talks highlighted practical applications, such as Amazon's robotic package handling, NA VER Labs' robotic deployment in South Korea, and marine data capture challenges by Abyss Solutions. Workshops explored industrial and agricultural computer vision systems."]