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[null,"1.","Record Nr.",null,"UNISA996472069703316"]
[null,null,"Titolo",null,"Pattern recognition and image analysis : 10th Iberian conference, IbPRIA 2022, Aveiro, Portugal, May 4-6, 2022, proceedings / / Armando J. Pinho [and three others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-04881-4"]
[null,null,"Descrizione fisica",null,"1 online resource (704 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.13256"]
[null,null,"Disciplina",null,"006.4"]
[null,null,"Soggetti",null,"Image processing - Digital techniques"]
[{"$nulls":4},"Optical pattern recognition"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Plenary Talks -- The Vision Subsystems in the TrimBot2020 Gardening Robot -- Speech as Personal Identifiable Information -- Machine Learning Security: Attacks and Defenses -- Invited Tutorials -- Speech Recognition and Machine Translation: From Bayes Decision Theory to Machine Learning and Deep Neural Networks -- Human 3D Sensing from Monocular Visual Data Using Classification Techniques -- Contents -- Document Analysis -- Test Sample Selection for Handwriting Recognition Through Language Modeling -- 1 Introduction -- 2 Methodology -- 2.1 Neural End-to-end Recognition Framework -- 2.2 Language Model -- 3 Experimental Setup -- 3.1 Corpora -- 3.2 Neural Architectures -- 3.3 Best Hypothesis Selection Policies -- 3.4 Evaluation Protocol -- 4 Results -- 5 Conclusions -- References -- Classification of Untranscribed Handwritten Notarial Documents by Textual Contents -- 1 Introduction -- 2 Probabilistic Indexing of Handwritten Text Images -- 3 Plain Text Document Classification -- 3.1 Feature Selection -- 3.2 Feature Extraction -- 4 Textual-Content-Based Classification of Sets of Images -- 4.1 Estimating Text Features from Image PrIx's -- 4.2 Estimating Information Gain and TfIdf of Sets of Text Images -- 4.3 Image Document Classification -- 5 Dataset and Experimental Settings -- 5.1 A Handwritten Notarial Document Dataset -- 5.2 Empirical Settings -- 6 Experiments and Results -- 7 Conclusion -- References -- Incremental Vocabularies in Machine Translation Through Aligned "]
[{"$nulls":4},"Embedding Projections -- 1 Introduction -- 2 Related Work -- 3 Models -- 3.1 Tokenization -- 3.2 Transformer Architecture -- 3.3 Projecting Vectors into Different Latent Spaces -- 4 Experimental Setup -- 4.1 Datasets -- 4.2 Training Details -- 4.3 Evaluation Metrics -- 5 Experimentation -- 5.1 Projecting Pre-trained Embeddings."]
[{"$nulls":4},"5.2 On the Importance of High-Quality Embeddings -- 5.3 Zero-shot Translation -- 6 Conclusions -- 7 Future Work -- References -- An Interactive Machine Translation Framework for Modernizing the Language of Historical Documents -- 1 Introduction -- 2 Related Work -- 3 Language Modernization Approaches -- 3.1 SMT Approach -- 3.2 NMT Approaches -- 4 Interactive Machine Translation -- 4.1 Prefix-Based IMT -- 4.2 Segment-Based IMT -- 5 Experimental Framework -- 5.1 User Simulation -- 5.2 Systems -- 5.3 Corpora -- 5.4 Metrics -- 6 Results -- 6.1 Quality Analysis -- 7 Conclusions and Future Work -- References -- From Captions to Explanations:pg A Multimodal Transformer-based Architecture for Natural Language Explanation Generation -- 1 Introduction -- 2 Related Work -- 2.1 Image Captioning -- 2.2 Natural Language Explanation Generation -- 3 Proposed Methodology -- 3.1 A Synthetic Dataset to Distinguish Captions from Explanations -- 3.2 An Encoder-Decoder Vision Transformer for Natural Language Explanation Generation -- 4 Results and Discussion -- 4.1 Image Captioning -- 4.2 Natural Language Explanations -- 5 Conclusion and Future Work -- References -- Medical Image Processing -- Diagnosis of Skin Cancer Using Hierarchical Neural Networks and Metadata -- 1 Introduction -- 2 Methodology -- 2.1 Flat Classifier -- 2.2 Hierarchical Classifier -- 2.3 Methods to Combine Images and Metadata -- 2.4 Selection Between Flat and Hierarchical Models -- 3 Results -- 3.1 Dataset -- 3.2 Performance Metrics -- 3.3 Impact of Metadata on Hierarchical and Flat Models -- 3.4 Comparison of the Mixed Models -- 3.5 Elimination of Classifiers (d) and (e) -- 3.6 Evaluation on Held-out Test Set -- 4 Conclusion -- References -- Lesion-Based Chest Radiography Image Retrieval for Explainability in Pathology Detection -- 1 Introduction -- 2 Methods -- 2.1 Dataset."]
[{"$nulls":4},"2.2 CXR Pathology Object Detection -- 2.3 Lesion-Based CXR Image Retrieval -- 2.4 Structural Similarity Lesion-Based CXR Image Retrieval -- 3 Experiments -- 3.1 CXR Pathology Object Detection -- 3.2 Lesion-Based CXR Image Retrieval -- 3.3 Quantitative Evaluation -- 3.4 Qualitative Evaluation -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Deep Learning for Diagnosis of Alzheimer's Disease with FDG-PET Neuroimaging -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 2D Slice-Level CNN Model -- 3.2 3D Subject-Level CNN Model -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 2D Slice-Level CNN for AD Diagnosis with PET Data -- 4.3 3D Subject-level CNN for AD Diagnosis with PET Data -- 5 Conclusions -- References -- Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Method -- 2.3 Evaluation -- 3 Results -- 4 Discussion and Conclusions -- References -- Increased Robustness in Chest X-Ray Classification Through Clinical Report-Driven Regularization -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Method -- 2.3 Evaluation -- 3 Results -- 4 Discussion and Conclusions -- References -- Medical Applications -- Deep Detection Models for Measuring Epidermal Bladder Cells -- 1 Introduction -- 2 Materials and Methods -- 2.1 Computational Methods -- 2.2 Experimental Study -- 3 Results -- 4 LabelGlandula -- 5 Case Study -- 6 Conclusions and Further Work -- References -- On the Performance of Deep Learning Models for Respiratory Sound Classification Trained on Unbalanced "]
[{"$nulls":4},"Data -- 1 Introduction -- 2 Data Description -- 3 Experimental Setup -- 3.1 Noise Filtering -- 3.2 Input Audio Representations -- 3.3 Sampling Methods -- 3.4 Data Augmentation -- 3.5 Classifiers -- 3.6 Validation and Metrics -- 4 Results and Discussion -- 5 Conclusions."]
[{"$nulls":4},"References -- Automated Adequacy Assessment of Cervical Cytology Samples Using Deep Learning -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 System Overview -- 3.2 Dataset -- 3.3 Experimental Setup -- 3.4 Model Evaluation -- 4 Results and Discussion -- 5 Conclusions and Future Work -- References -- Exploring Alterations in Electrocardiogram During the Postoperative Pain -- 1 Introduction -- 2 Related Literature -- 3 Methods -- 3.1 Data Collection -- 3.2 Preprocessing -- 3.3 Feature Extraction and Transformation -- 3.4 Univariate Approach -- 3.5 Unsupervised Multivariate Approach -- 4 Results -- 4.1 Dataset -- 4.2 Univariate Approach -- 4.3 Unsupervised Multivariate Approach -- 5 Discussion -- 6 Conclusions and Future Work -- References -- Differential Gene Expression Analysis of the Most Relevant Genes for Lung Cancer Prediction and Sub-type Classification -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset and Preprocessing -- 2.2 Cancer and Subtype Classification -- 2.3 Differential Expressed Genes Analysis -- 3 Results and Discussion -- 3.1 Classification Results -- 3.2 Differential Expressed Genes Analysis -- 4 Conclusions -- References -- Detection of Epilepsy in EEGs Using Deep Sequence Models - A Comparative Study -- 1 Introduction -- 2 Methods -- 2.1 EEG Data and Pre-processing -- 2.2 Database Structuring -- 2.3 Deep Learning Models -- 2.4 Performance Assessment -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Biometrics -- Facial Emotion Recognition for Sentiment Analysis of Social Media Data -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Image Classifier -- 3.2 Salient Area Detector -- 3.3 Text Classification -- 3.4 Facial Expression Recognition Module -- 3.5 Decision Fusion -- 4 Experiments -- 4.1 Training the Facial Expression Recognition Model -- 4.2 Data Set for the Full Model Evaluation."]
[{"$nulls":4},"4.3 Results -- 5 Conclusions -- References -- Heartbeat Selection Based on Outlier Removal -- 1 Introduction -- 2 Proposed Approach -- 2.1 Modified DMEAN: Formal Description -- 2.2 Algorithm Tunning: Feature and Threshold Selection -- 2.3 Normalized Cross-Correlation -- 3 Methodology -- 3.1 Pre-processing -- 3.2 Modified DMEAN in ECG Biometrics -- 4 Performance Analysis -- 4.1 Performance Analysis Between Modified DMEAN and DMEAN -- 4.2 Performance Analysis of Modified DMEAN and NCC -- 5 Conclusions -- References -- Characterization of Emotions Through Facial Electromyogram Signals -- 1 Introduction -- 2 Dataset and Methodology -- 2.1 Dataset -- 2.2 Methodology -- 3 Results -- 4 Conclusion -- References -- Multimodal Feature Evaluation and Fusion for Emotional Well-Being Monitorization -- 1 Introduction -- 2 Task and Corpus -- 3 Feature Analysis -- 3.1 Semantic and Paralinguistic Information -- 3.2 Clustering -- 3.3 Novel Techniques for Semantic Information Extraction -- 4 Supervised Learning for Classification Experiments -- 4.1 Random Forest -- 4.2 ANN -- 4.3 BERT -- 4.4 Results -- 5 Conclusions and Future Work -- References -- Temporal Convolutional Networks for Robust Face Liveness Detection -- 1 Introduction -- 2 Related Work -- 2.1 Non-intrusive Pulse Extraction -- 2.2 Face Liveness Detection -- 3 Robust Liveness Detection on the Fly -- 4 Temporal Convolutional Neural Networks for Liveness Detection -- 4.1 Regular Convolution TCN Block -- 4.2 Dilated Convolution TCN Block -- 5 Evaluation -- 5.1 Dataset -- 5.2 Protocol and Implementation Details -- 5.3 Results -- 5.4 TCN Model Training Analysis -- 6 Conclusions -- References -- "]
[{"$nulls":4},"Pattern Recognition and Machine Learning -- MaxDropoutV2: An Improved Method to Drop Out Neurons in Convolutional Neural Networks -- 1 Introduction -- 2 Related Works -- 3 MaxDropoutV2 as an Improved Version of MaxDropout."]
[{"$nulls":4},"3.1 MaxDropout."]