Información del autor
Autor Papa, João Paulo |
Documentos disponibles escritos por este autor (3)
Crear una solicitud de compra Refinar búsquedaDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support / Stoyanov, Danail ; Taylor, Zeike ; Carneiro, Gustavo ; Syeda-Mahmood, Tanveer ; Martel, Anne ; Maier-Hein, Lena ; Tavares, João Manuel RS ; Bradley, Andrew ; Papa, João Paulo ; Belagiannis, Vasileios ; Nascimento, Jacinto C. ; Lu, Zhi ; Conjeti, Sailesh ; Moradi, Mehdi ; Greenspan, Hayit ; Madabhushi, Anant
TÃtulo : Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings Tipo de documento: documento electrónico Autores: Stoyanov, Danail, ; Taylor, Zeike, ; Carneiro, Gustavo, ; Syeda-Mahmood, Tanveer, ; Martel, Anne, ; Maier-Hein, Lena, ; Tavares, João Manuel RS, ; Bradley, Andrew, ; Papa, João Paulo, ; Belagiannis, Vasileios, ; Nascimento, Jacinto C., ; Lu, Zhi, ; Conjeti, Sailesh, ; Moradi, Mehdi, ; Greenspan, Hayit, ; Madabhushi, Anant, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XVII, 387 p. 197 ilustraciones, 149 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-00889-5 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Informática Médica Ciencias sociales Protección de datos Informática de la Salud Computadoras y Educación Aplicación informática en ciencias sociales y del comportamiento. Seguridad de datos e información Clasificación: 006.3 Resumen: Este libro constituye las actas conjuntas arbitradas del 4.º Taller internacional sobre aprendizaje profundo en análisis de imágenes médicas, DLMIA 2018, y el 8.º Taller internacional sobre aprendizaje multimodal para el apoyo a las decisiones clÃnicas, ML-CDS 2018, celebrado junto con la 21.ª Conferencia internacional sobre Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, en Granada, España, en septiembre de 2018. Los 39 artÃculos completos presentados en DLMIA 2018 y los 4 artÃculos completos presentados en ML-CDS 2018 fueron cuidadosamente revisados ​​y seleccionados entre 85 presentaciones a DLMIA y 6 presentaciones a ML-CDS. Los artÃculos de DLMIA se centran en el diseño y uso de métodos de aprendizaje profundo en imágenes médicas. Los artÃculos de ML-CDS analizan nuevas técnicas de extracción/recuperación multimodal y su uso en el apoyo a las decisiones clÃnicas. Nota de contenido: Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior -- Weakly Supervised Localisation for Fetal Ultrasound Images -- Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images -- Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks -- Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease -- Contextual Additive Networks to Efficiently Boost 3D Image Segmentations -- Longitudinal detection of radiological abnormalities with time-modulated LSTM -- SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays -- Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy -- Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-Organizing Maps -- Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images -- Deep semi-supervised segmentation with weight-averaged consistency targets -- Focal Dice Loss and Image Dilation for Brain Tumor Segmentation -- Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography -- Unsupervised feature learning for outlier detection with stacked convolutional autoencoders, siamese networks and Wasserstein autoencoders: application to epilepsy detection -- Automatic myocardial strain imaging in echocardiography using deep learning -- 3D Convolutional Neural Networks for Classification of Functional Connectomes -- Computed Tomography Image Enhancement using 3D Convolutional Neural Network -- Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning -- A Unified Framework Integrating Recurrent Fully-convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data -- Learning Optimal Deep Projection of 18 F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes -- Learning to Segment Medical Images with Scribble-Supervision Alone -- Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration -- TreeNet: Multi-Loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees -- Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation -- UOLO - automatic object detection and segmentation in biomedical images -- Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks -- Multi-Scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification -- Nonlinear adaptively learned optimization for object localization in 3D medical images -- Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network -- UNet++: A Nested U-Net Architecture for Medical Image Segmentation -- MTMR-Net: Multi-Task Deep Learning with Margin Ranking Lossfor Lung Nodule Analysis -- PIMMS: Permutation Invariant Multi-Modal Segmentation -- Handling Missing Annotations for Semantic Segmentation with Deep ConvNets -- 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation -- ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans -- Unpaired Deep Cross-modality Synthesis with Fast Training -- Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification -- Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN -- A Multi-Scale Multiple Sclerosis Lesion Change Detection in a Multi-Sequence MRI -- Multi-task Sparse Low-rank Learning for Multi-classification of Parkinson's Disease -- Optic Disc segmentation in Retinal Fundus Images using Fully Convolutional Network and Removal of False-positives Based on Shape Features -- Integrating deformable modeling with 3D deep neural network segmentation. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings [documento electrónico] / Stoyanov, Danail, ; Taylor, Zeike, ; Carneiro, Gustavo, ; Syeda-Mahmood, Tanveer, ; Martel, Anne, ; Maier-Hein, Lena, ; Tavares, João Manuel RS, ; Bradley, Andrew, ; Papa, João Paulo, ; Belagiannis, Vasileios, ; Nascimento, Jacinto C., ; Lu, Zhi, ; Conjeti, Sailesh, ; Moradi, Mehdi, ; Greenspan, Hayit, ; Madabhushi, Anant, . - 1 ed. . - [s.l.] : Springer, 2018 . - XVII, 387 p. 197 ilustraciones, 149 ilustraciones en color.
ISBN : 978-3-030-00889-5
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Informática Médica Ciencias sociales Protección de datos Informática de la Salud Computadoras y Educación Aplicación informática en ciencias sociales y del comportamiento. Seguridad de datos e información Clasificación: 006.3 Resumen: Este libro constituye las actas conjuntas arbitradas del 4.º Taller internacional sobre aprendizaje profundo en análisis de imágenes médicas, DLMIA 2018, y el 8.º Taller internacional sobre aprendizaje multimodal para el apoyo a las decisiones clÃnicas, ML-CDS 2018, celebrado junto con la 21.ª Conferencia internacional sobre Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, en Granada, España, en septiembre de 2018. Los 39 artÃculos completos presentados en DLMIA 2018 y los 4 artÃculos completos presentados en ML-CDS 2018 fueron cuidadosamente revisados ​​y seleccionados entre 85 presentaciones a DLMIA y 6 presentaciones a ML-CDS. Los artÃculos de DLMIA se centran en el diseño y uso de métodos de aprendizaje profundo en imágenes médicas. Los artÃculos de ML-CDS analizan nuevas técnicas de extracción/recuperación multimodal y su uso en el apoyo a las decisiones clÃnicas. Nota de contenido: Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior -- Weakly Supervised Localisation for Fetal Ultrasound Images -- Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images -- Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks -- Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease -- Contextual Additive Networks to Efficiently Boost 3D Image Segmentations -- Longitudinal detection of radiological abnormalities with time-modulated LSTM -- SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays -- Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy -- Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-Organizing Maps -- Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images -- Deep semi-supervised segmentation with weight-averaged consistency targets -- Focal Dice Loss and Image Dilation for Brain Tumor Segmentation -- Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography -- Unsupervised feature learning for outlier detection with stacked convolutional autoencoders, siamese networks and Wasserstein autoencoders: application to epilepsy detection -- Automatic myocardial strain imaging in echocardiography using deep learning -- 3D Convolutional Neural Networks for Classification of Functional Connectomes -- Computed Tomography Image Enhancement using 3D Convolutional Neural Network -- Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning -- A Unified Framework Integrating Recurrent Fully-convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data -- Learning Optimal Deep Projection of 18 F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes -- Learning to Segment Medical Images with Scribble-Supervision Alone -- Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration -- TreeNet: Multi-Loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees -- Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation -- UOLO - automatic object detection and segmentation in biomedical images -- Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks -- Multi-Scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification -- Nonlinear adaptively learned optimization for object localization in 3D medical images -- Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network -- UNet++: A Nested U-Net Architecture for Medical Image Segmentation -- MTMR-Net: Multi-Task Deep Learning with Margin Ranking Lossfor Lung Nodule Analysis -- PIMMS: Permutation Invariant Multi-Modal Segmentation -- Handling Missing Annotations for Semantic Segmentation with Deep ConvNets -- 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation -- ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans -- Unpaired Deep Cross-modality Synthesis with Fast Training -- Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification -- Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN -- A Multi-Scale Multiple Sclerosis Lesion Change Detection in a Multi-Sequence MRI -- Multi-task Sparse Low-rank Learning for Multi-classification of Parkinson's Disease -- Optic Disc segmentation in Retinal Fundus Images using Fully Convolutional Network and Removal of False-positives Based on Shape Features -- Integrating deformable modeling with 3D deep neural network segmentation. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings Tipo de documento: documento electrónico Autores: Cardoso, M. Jorge, ; Arbel, Tal, ; Carneiro, Gustavo, ; Syeda-Mahmood, Tanveer, ; Tavares, João Manuel RS, ; Moradi, Mehdi, ; Bradley, Andrew, ; Greenspan, Hayit, ; Papa, João Paulo, ; Madabhushi, Anant, ; Nascimento, Jacinto C., ; Cardoso, Jaime S., ; Belagiannis, Vasileios, ; Lu, Zhi, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XIX, 385 p. 169 ilustraciones ISBN/ISSN/DL: 978-3-319-67558-9 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Inteligencia artificial Informática Médica Bioinformática diseño lógico Informática de la Salud BiologÃa Computacional y de Sistemas Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Tercer Taller Internacional sobre Aprendizaje Profundo en Análisis de Imágenes Médicas, DLMIA 2017, y el 6º Taller Internacional sobre Aprendizaje Multimodal para el Apoyo a la Decisión ClÃnica, ML-CDS 2017, celebrado junto con la 20ª Conferencia Internacional sobre Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, en la ciudad de Québec, QC, Canadá, en septiembre de 2017. Los 38 artÃculos completos presentados en DLMIA 2017 y los 5 artÃculos completos presentados en ML-CDS 2017 fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de DLMIA se centran en el diseño y uso de métodos de aprendizaje profundo en imágenes médicas. Los artÃculos de ML-CDS analizan nuevas técnicas de extracción/recuperación multimodal y su uso en el apoyo a las decisiones clÃnicas. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings [documento electrónico] / Cardoso, M. Jorge, ; Arbel, Tal, ; Carneiro, Gustavo, ; Syeda-Mahmood, Tanveer, ; Tavares, João Manuel RS, ; Moradi, Mehdi, ; Bradley, Andrew, ; Greenspan, Hayit, ; Papa, João Paulo, ; Madabhushi, Anant, ; Nascimento, Jacinto C., ; Cardoso, Jaime S., ; Belagiannis, Vasileios, ; Lu, Zhi, . - 1 ed. . - [s.l.] : Springer, 2017 . - XIX, 385 p. 169 ilustraciones.
ISBN : 978-3-319-67558-9
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Inteligencia artificial Informática Médica Bioinformática diseño lógico Informática de la Salud BiologÃa Computacional y de Sistemas Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Tercer Taller Internacional sobre Aprendizaje Profundo en Análisis de Imágenes Médicas, DLMIA 2017, y el 6º Taller Internacional sobre Aprendizaje Multimodal para el Apoyo a la Decisión ClÃnica, ML-CDS 2017, celebrado junto con la 20ª Conferencia Internacional sobre Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, en la ciudad de Québec, QC, Canadá, en septiembre de 2017. Los 38 artÃculos completos presentados en DLMIA 2017 y los 5 artÃculos completos presentados en ML-CDS 2017 fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de DLMIA se centran en el diseño y uso de métodos de aprendizaje profundo en imágenes médicas. Los artÃculos de ML-CDS analizan nuevas técnicas de extracción/recuperación multimodal y su uso en el apoyo a las decisiones clÃnicas. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers / Tipo de documento: documento electrónico Autores: Tavares, João Manuel R. S., ; Papa, João Paulo, ; González Hidalgo, Manuel, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XV, 490 p. 167 ilustraciones, 135 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-93420-0 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Sistemas de reconocimiento de patrones Aprendizaje automático Visión por computador IngenierÃa Informática Red de computadoras Software de la aplicacion Reconocimiento de patrones automatizado IngenierÃa Informática y Redes Aplicaciones informáticas y de sistemas de información Clasificación: 006.4 Resumen: Este libro constituye las actas del 25º Congreso Iberoamericano sobre Progresos en Reconocimiento de Patrones, Análisis de Imágenes, Visión por Computador y Aplicaciones, CIARP 2021, que tuvo lugar del 10 al 13 de mayo de 2021. Inicialmente se planeó que la conferencia se llevara a cabo en Oporto, Portugal, pero cambió a un evento virtual debido a la pandemia de COVID-19. Los 45 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 82 presentaciones. Estaban organizados en secciones temáticas de la siguiente manera: aplicaciones médicas; procesamiento natural del lenguaje; metaheurÃsticas; Segmentación de imagen; bases de datos; aprendizaje profundo; inteligencia artificial explicable; procesamiento de imágenes; aprendizaje automático; y visión por computadora. . Nota de contenido: Medical Applications -- Predicting the use of Invasive Mechanical Ventilation in ICU COVID-19 patients -- A Coarse to Fine Corneal Ulcer Segmentation Approach using U-net and DexiNed in Chain -- Replacing Data Augmentation with Rotation-equivariant CNNs in Image-based Classification of Oral Cancer -- A Multitasking Learning Framework for Dermoscopic Image Analysis -- An Evaluation of Segmentation Techniques for COVID-19 Identification in Chest X-Ray -- A Study on Annotation Efficient Learning Methods for Segmentation in Prostate Histopathological Images -- Natural Language Processing -- Data-Augmented Emoji Approach to Sentiment Classification of Tweets -- Detecting Hate Speech in Cross-Lingual and Multi-Lingual Settings Using Language Agnostic Representations -- Prediction of Perception of Security Using Social Media Content -- Metaheuristics -- Fine-Tuning Dropout Regularization in Energy-Based Deep Learning -- Enhancing Hyper-To-Real Space Projections Through Euclidean NormMeta-Heuristic Optimization -- Using Particle Swarm Optimization With Gradient Descent For Parameter Learning In Convolutional Neural Networks -- Image Segmentation -- Object Delineation by Iterative Dynamic Trees -- Low-Cost Domain Adaptation for Crop and Weed Segmentation -- Databases -- MIGMA: The Facial Emotion Image Dataset forHuman Expression Recognition -- Construction of Brazilian Regulatory Traffic Sign Recognition Dataset -- Japanese Kana and Brazilian Portuguese manuscript database -- Skelibras: a Large 2d Skeleton Dataset of Dynamic Brazilian Signs -- Deep Learning -- Cricket Scene Analysis using the RetinaNet architecture. -Texture-Based Image Transformations for Improved Deep Learning Classification -- Towards Precise Recognition of Pollen Bearing Bees by Convolutional Neural Networks -- Web Application Attacks Detection Using Deep Learning -- Less is More: Accelerating Faster Neural Networks Straight from JPEG -- Optimizing Person Re-Identification using GeneratedAttention Masks -- Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing -- Explainable Artificial Intelligence -- Interpretable Concept Drift -- Interpreting a Conditional Generative Adversarial Network Model for Crime Prediction -- Interpreting Decision Patterns in Financial Applications -- Image Processing -- Metal Artifact Reduction based on color mapping and inpainting techniques -- New Improvement in Obtaining Monogenic Phase Congruency -- Machine Learning -- Evaluating the Construction of Feature Descriptors in the Performance of the Image Data Stream Classification -- Clustering-based Partitioning of Water Distribution Networks for Leak Zone Location -- Bias Quantification for Protected Features in Pattern Classification Problems -- Regional Commodities Price Volatility Assessment Using Self-Driven Recurrent Networks -- Semi-supervised Deep Learning Based on Label Propagation in a 2D Embedded Space -- Iterative Creation of Matching-Graphs - Finding Relevant Substructures in Graph Sets -- Semi-Autogeonous (SAG) Mill Overload Forecasting -- Novel Time-Frequency Based Scheme for Detecting Sound Events from Sound Background in Audio Segments -- Computer Vision -- Generalized Conics with the Sharp Corners -- Automatic Face Mask Detection using a Hide and Seek Algorithm -- A Feature Extraction Approach Based on LBP Operator and Complex Networks for Face Recognition -- End-to-End Deep Sketch-to-Photo Matching Enforcing Realistic Photo Generation -- Forensic Analysis of Tampered Digital Photos -- COVID-19 Lung CT Images Recognition: A Feature-Based Approach -- A Topologically Consistent Color Digital Image Representation by a Single Tree. Tipo de medio : Computadora Summary : This book constitutes the proceedings of the 25th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2021, which took place during May 10–13, 2021. The conference was initially planned to take place in Porto, Portugal, but changed to a virtual event due to the COVID-19 pandemic. The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They were organized in topical sections as follows: medical applications; natural language processing; metaheuristics; image segmentation; databases; deep learning; explainable artificial intelligence; image processing; machine learning; and computer vision. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications : 25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers / [documento electrónico] / Tavares, João Manuel R. S., ; Papa, João Paulo, ; González Hidalgo, Manuel, . - 1 ed. . - [s.l.] : Springer, 2021 . - XV, 490 p. 167 ilustraciones, 135 ilustraciones en color.
ISBN : 978-3-030-93420-0
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Sistemas de reconocimiento de patrones Aprendizaje automático Visión por computador IngenierÃa Informática Red de computadoras Software de la aplicacion Reconocimiento de patrones automatizado IngenierÃa Informática y Redes Aplicaciones informáticas y de sistemas de información Clasificación: 006.4 Resumen: Este libro constituye las actas del 25º Congreso Iberoamericano sobre Progresos en Reconocimiento de Patrones, Análisis de Imágenes, Visión por Computador y Aplicaciones, CIARP 2021, que tuvo lugar del 10 al 13 de mayo de 2021. Inicialmente se planeó que la conferencia se llevara a cabo en Oporto, Portugal, pero cambió a un evento virtual debido a la pandemia de COVID-19. Los 45 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 82 presentaciones. Estaban organizados en secciones temáticas de la siguiente manera: aplicaciones médicas; procesamiento natural del lenguaje; metaheurÃsticas; Segmentación de imagen; bases de datos; aprendizaje profundo; inteligencia artificial explicable; procesamiento de imágenes; aprendizaje automático; y visión por computadora. . Nota de contenido: Medical Applications -- Predicting the use of Invasive Mechanical Ventilation in ICU COVID-19 patients -- A Coarse to Fine Corneal Ulcer Segmentation Approach using U-net and DexiNed in Chain -- Replacing Data Augmentation with Rotation-equivariant CNNs in Image-based Classification of Oral Cancer -- A Multitasking Learning Framework for Dermoscopic Image Analysis -- An Evaluation of Segmentation Techniques for COVID-19 Identification in Chest X-Ray -- A Study on Annotation Efficient Learning Methods for Segmentation in Prostate Histopathological Images -- Natural Language Processing -- Data-Augmented Emoji Approach to Sentiment Classification of Tweets -- Detecting Hate Speech in Cross-Lingual and Multi-Lingual Settings Using Language Agnostic Representations -- Prediction of Perception of Security Using Social Media Content -- Metaheuristics -- Fine-Tuning Dropout Regularization in Energy-Based Deep Learning -- Enhancing Hyper-To-Real Space Projections Through Euclidean NormMeta-Heuristic Optimization -- Using Particle Swarm Optimization With Gradient Descent For Parameter Learning In Convolutional Neural Networks -- Image Segmentation -- Object Delineation by Iterative Dynamic Trees -- Low-Cost Domain Adaptation for Crop and Weed Segmentation -- Databases -- MIGMA: The Facial Emotion Image Dataset forHuman Expression Recognition -- Construction of Brazilian Regulatory Traffic Sign Recognition Dataset -- Japanese Kana and Brazilian Portuguese manuscript database -- Skelibras: a Large 2d Skeleton Dataset of Dynamic Brazilian Signs -- Deep Learning -- Cricket Scene Analysis using the RetinaNet architecture. -Texture-Based Image Transformations for Improved Deep Learning Classification -- Towards Precise Recognition of Pollen Bearing Bees by Convolutional Neural Networks -- Web Application Attacks Detection Using Deep Learning -- Less is More: Accelerating Faster Neural Networks Straight from JPEG -- Optimizing Person Re-Identification using GeneratedAttention Masks -- Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing -- Explainable Artificial Intelligence -- Interpretable Concept Drift -- Interpreting a Conditional Generative Adversarial Network Model for Crime Prediction -- Interpreting Decision Patterns in Financial Applications -- Image Processing -- Metal Artifact Reduction based on color mapping and inpainting techniques -- New Improvement in Obtaining Monogenic Phase Congruency -- Machine Learning -- Evaluating the Construction of Feature Descriptors in the Performance of the Image Data Stream Classification -- Clustering-based Partitioning of Water Distribution Networks for Leak Zone Location -- Bias Quantification for Protected Features in Pattern Classification Problems -- Regional Commodities Price Volatility Assessment Using Self-Driven Recurrent Networks -- Semi-supervised Deep Learning Based on Label Propagation in a 2D Embedded Space -- Iterative Creation of Matching-Graphs - Finding Relevant Substructures in Graph Sets -- Semi-Autogeonous (SAG) Mill Overload Forecasting -- Novel Time-Frequency Based Scheme for Detecting Sound Events from Sound Background in Audio Segments -- Computer Vision -- Generalized Conics with the Sharp Corners -- Automatic Face Mask Detection using a Hide and Seek Algorithm -- A Feature Extraction Approach Based on LBP Operator and Complex Networks for Face Recognition -- End-to-End Deep Sketch-to-Photo Matching Enforcing Realistic Photo Generation -- Forensic Analysis of Tampered Digital Photos -- COVID-19 Lung CT Images Recognition: A Feature-Based Approach -- A Topologically Consistent Color Digital Image Representation by a Single Tree. Tipo de medio : Computadora Summary : This book constitutes the proceedings of the 25th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2021, which took place during May 10–13, 2021. The conference was initially planned to take place in Porto, Portugal, but changed to a virtual event due to the COVID-19 pandemic. The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They were organized in topical sections as follows: medical applications; natural language processing; metaheuristics; image segmentation; databases; deep learning; explainable artificial intelligence; image processing; machine learning; and computer vision. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
