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[null,"1.","Record Nr.",null,"UNINA9910766892403321"]
[null,null,"Autore",null,"Vasconcelos Verónica"]
[null,null,"Titolo",null,"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 26th Iberoamerican Congress, CIARP 2023, Coimbra, Portugal, November 27–30, 2023, Proceedings, Part I / / edited by Verónica Vasconcelos, Inês Domingues, Simão Paredes"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"9783031490187"]
[{"$nulls":4},"3031490185"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (764 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14469"]
[null,null,"Disciplina",null,"006.4"]
[null,null,"Soggetti",null,"Pattern recognition systems"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Automated Pattern Recognition"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Deblur Capsule Networks -- 1 Introduction -- 2 Related Works -- 3 Deblur Capsule Networks -- 3.1 Blur Type Classification -- 3.2 Point Spread Function Reconstruction -- 3.3 Image Deep Regularized Deconvolution -- 4 Experiments and Analysis -- 4.1 DbCN Optimization Procedure -- 4.2 Synthetic Camera Motion Blur -- 4.3 Synthetic Multi-domain Blur -- 4.4 Ablation Study -- 5 Conclusions and Future Works -- References -- Graph Embedding of Almost Constant Large Graphs -- 1 Introduction -- 2 Graphs and Graph Embedding -- 3 GraphFingerprint: An Embedding for Almost Constant Graphs -- 3.1 Algorithm Input Parameters -- 3.2 Local Substructures -- 3.3 GraphFingerprint Definition -- 3.4 GraphFingerprint Examples "]
[{"$nulls":4},"-- 4 Experimental Section -- 4.1 From Metal-Oxide Nanocompound to GraphFingerprint -- 4.2 Toxicity Prediction Based on Global Features -- 4.3 Toxicity Prediction Based on GraphFingerprints -- 4.4 Toxicity Prediction Based on Global Features and GraphFingerprints -- 5 Conclusions -- 6 Future Work -- References -- Feature Importance for Clustering -- 1 Introduction -- 2 Cluster Analysis -- 3 Proposed Methods -- 3.1 Prototype-Based Feature Importance -- 3.2 SHAP-Based Feature Importance -- 4 Experimental Simulations -- 5 Concluding Remarks -- References -- Uncovering Manipulated Files Using Mathematical Natural Laws -- 1 Introduction -- 2 Related Work -- 3 Benford's Law Fundamentals -- 3.1 Benford's Law Statement -- 4 Dataset -- 5 Benford's Law-Based Method -- 5.1 Pre-processing -- 5.2 Processing -- 5.3 Median Absolute Deviation -- 5.4 Evaluation Metrics -- 6 Results -- 6.1 Analysis of Results -- 7 Conclusions and Future Work -- References -- History Based Incremental Singular Value Decomposition for Background Initialization and Foreground Segmentation -- 1 Introduction."]
[{"$nulls":4},"2 Related Work -- 2.1 Identification of SFOs -- 2.2 Background Dependency -- 3 Methodology -- 3.1 Notation and Preliminaries -- 3.2 Computation of Incremental SVD -- 3.3 History Based Incremental SVD (hi-SVD) -- 4 Experimental Results -- 4.1 Datasets -- 4.2 SFO Status Identification Experiment -- 4.3 Foreground Segmentation Experiment -- 5 Conclusion -- References -- Vehicle Re-Identification Based on Unsupervised Domain Adaptation by Incremental Generation of Pseudo-Labels -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 DBSCAN Pseudo-Labels -- 3.2 Fine-Tuning -- 3.3 Unsupervised Domain Adaptation -- 4 Experimental Validation -- 4.1 Dataset and Evaluation Environment -- 4.2 Implementation Details -- 4.3 Ablation Study for Eps-Neighborhood in DBSCAN -- 4.4 Ablation Study for Increasing the Number of Cycles in the Generation of Pseudo-Labels -- 5 Conclusions -- References -- How to Turn Your Camera into a Perfect Pinhole Model -- 1 Introduction -- 2 Methods -- 2.1 Gaussian Processes -- 2.2 Constructing an Ideal Pinhole Camera -- 2.3 The Datasets -- 3 Results -- 3.1 Collineation Assumption -- 3.2 Reprojection Error -- 3.3 Distortion Removal -- 4 Discussion -- 5 Conclusion -- A Zhang's Method -- B Simplified Zhang's Method -- References -- Single Image HDR Synthesis with Histogram Learning -- 1 Introduction -- 2 Method -- 2.1 LDR2EDR by Histogram and Resolution difference -- 2.2 EDR2HDR by Cumulative Histogram Learning -- 2.3 Fine-Tuning with Reinforcement Learning -- 3 Experiments -- 4 Conclusion -- References -- But That's Not Why: Inference Adjustment by Interactive Prototype Revision -- 1 Introduction -- 2 Prototype-Based Learning -- 3 Interactive Prototype Revision -- 4 Results -- 5 Conclusion -- References -- Teaching Practices Analysis Through Audio Signal Processing -- 1 Introduction -- 2 Related Work -- 3 Dataset Description."]
[{"$nulls":4},"4 Classroom Activity Detection -- 4.1 Training and Testing Data -- 4.2 Unsupervised Diarization Approach -- 4.3 Supervised Audio Classification -- 4.4 Experiments and Results -- 5 Additional Tools for English Lessons Analysis -- 5.1 Language Detection -- 5.2 Key Phrases Matching -- 5.3 User Interface for Education Technicians -- 6 Conclusions and Further Work -- References -- Time Distributed Multiview Representation for Speech Emotion Recognition -- 1 Introduction -- 2 Related Work -- 3 Proposed Strategy -- 3.1 Database Description -- 3.2 General Architecture -- 3.3 Step 1 - Initial Procedures -- 3.4 Steps 2 and 3 - Algorithms and Combiner -- 3.5 Steps 4 and 5 - LSTM and Emotion Classification -- 4 Experimental Results -- 4.1 Experiment 1 - Results for All RAVDESS Database -- 4.2 "]
[{"$nulls":4},"Experiment 2 - Database Divided by Intensity -- 4.3 Experiment 3 - LOSO Protocol -- 5 Discussions -- 6 Conclusion -- References -- Detection of Covid-19 in Chest X-Ray Images Using Percolation Features and Hermite Polynomial Classification -- 1 Introduction -- 2 Materials and Methods -- 2.1 Image Database -- 2.2 Methodology -- 3 Results and Discussion -- 3.1 Feature Evaluation -- 3.2 Performance of the HP Classifier -- 4 Conclusion -- References -- Abandoned Object Detection Using Persistent Homology -- 1 Introduction -- 2 From Background Subtraction to Simplicial Complex -- 3 Surveillance Points -- 4 Filtration -- 5 Persistent Homology and Topological Signature -- 6 Detecting Abandoned Objects -- 7 Experimental Results -- 8 Conclusion and Future Works -- References -- Interactive Segmentation with Incremental Watershed Cuts -- 1 Introduction -- 2 Watershed Cuts -- 3 Semi-supervised Watershed Cut Algorithm with Interactions -- 3.1 Tree-Node Marking -- 3.2 Pixel Labeling -- 3.3 Incremental Workflow -- 4 Experiments -- 4.1 Experiment with User Generated Seeds."]
[{"$nulls":4},"4.2 Experiment with Randomly Generated Seeds -- 5 Conclusion -- References -- Supervised Learning of Hierarchical Image Segmentation -- 1 Introduction -- 2 Ultrametric Dataset -- 3 Model -- 4 Evaluation Metrics -- 5 Experiments -- 6 Conclusion -- References -- Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification -- 1 Introduction -- 2 Super-Resolution -- 2.1 Super-Resolution Convolution Neural Network (SRCNN) -- 2.2 Modified 3D Residual-in-Residual Dense Block (m3DRRDB) -- 2.3 SwinIR Transformer -- 3 Scene Classification -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 5 Results and Discussion -- 5.1 Experiment 1: Ranking of Super-Resolution Methods -- 5.2 Experiment 2: Impact of Super-Resolution on Aerial Scene Classification -- 6 Conclusion -- References -- Weeds Classification with Deep Learning: An Investigation Using CNN, Vision Transformers, Pyramid Vision Transformers, and Ensemble Strategy -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Datasets -- 3.2 Models -- 3.3 Ensemble -- 3.4 Evaluation -- 3.5 Training Configuration -- 3.6 Execution Environment -- 4 Results and Discussion -- 5 Conclusions -- References -- Leveraging Question Answering for Domain-Agnostic Information Extraction -- 1 Introduction -- 2 Related Work -- 3 Question Answering for Information Extraction -- 3.1 Models and Questions -- 3.2 Approach -- 3.3 Visual Explainability on Evaluation -- 4 Case Studies -- 4.1 Application to Toxicology Analysis -- 4.2 Application to Finance -- 5 Conclusion -- References -- Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves -- 1 Introduction -- 2 Dataset Collection -- 3 Proposed Approach -- 3.1 Image-to-Text Track -- 3.2 Localization Track -- 3.3 Selection Track -- 3.4 Report Generation -- 4 Results -- 5 Conclusions and Future Works."]
[{"$nulls":4},"References -- A Self-Organizing Map Clustering Approach to Support Territorial Zoning -- 1 Introduction -- 2 Related Work -- 2.1 Zoning with Self-Organizing Maps -- 2.2 Clustering Ordinal Categorical Data -- 3 Material and Methods -- 3.1 The Alto Taquari Basin - MS/MT, Brazil -- 3.2 Clustering Categorical Ordinal Data -- 3.3 Clustering Assessing -- 4 Results and Discussion -- 5 Conclusions -- References -- Spatial-Temporal Graph Transformer for Surgical Skill Assessment in Simulation Sessions -- 1 Introduction -- 2 Proposed Approach -- 2.1 Spectral Graph Convolutional Networks -- 2.2 Transformer Encoder -- 2.3 Surgical Skill Classifier -- 3 Experimental Results -- 3.1 Dataset -- 3.2 Data Preprocessing -- 3.3 Results -- 4 Conclusion -- References -- Deep Learning in the Identification of Psoriatic Skin Lesions -- 1 "]
[{"$nulls":4},"Introduction -- 2 Background and Literature Review -- 3 Methodology -- 3.1 Understanding the Problem -- 3.2 Deep Learning -- 3.3 Dataset -- 3.4 Classification Architectures -- 3.5 Training -- 4 Results -- 4.1 CLAHE -- 4.2 Type of Input Image -- 4.3 Data Augmentation -- 5 Discussion -- 6 Mobile Application -- 7 Conclusion -- References -- WildFruiP: Estimating Fruit Physicochemical Parameters from Images Captured in the Wild -- 1 Introduction -- 2 Related Work -- 2.1 Detection Methods -- 2.2 Segmentation Methods -- 2.3 Methods for Estimating Fruit Ripeness in Images -- 3 Proposed Method -- 3.1 Fruit Detection and Segmentation -- 3.2 Image Alignment -- 3.3 Determination of Physicochemical Parameters -- 4 Dataset -- 5 Experiments -- 5.1 Implementation Details -- 5.2 Metrics -- 5.3 Performance of the Proposed Approach -- 5.4 Impact of Alignment Phase -- 5.5 Impact of Model Architecture -- 5.6 Hard Samples -- 6 Conclusion and Future Work Prospects -- References."]
[{"$nulls":4},"Depression Detection Using Deep Learning and Natural Language Processing Techniques: A Comparative Study."]
[null,null,null,"Sommario/riassunto","This 2-volume set, LNCS 14469 and 14470, constitutes the proceedings of the 26th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2023, which took place in Coimbra, Portugal, in November 2023. The 61 papers presented were carefully reviewed and selected from 106 submissions. And present research in the fields of pattern recognition, artificial intelligence, and related areas."]