9th IAPR TC3 Workshop, ANNPR 2020, Winterthur, Switzerland, September 2–4, 2020, Proceedings / Schilling, Frank-Peter ; Stadelmann, Thilo  

Público ISBD Ningún comentario para este título. Título : 9th IAPR TC3 Workshop, ANNPR 2020, Winterthur, Switzerland, September 2–4, 2020, Proceedings Tipo de documento: documento electrónico Autores: Schilling, Frank-Peter, ; Stadelmann, Thilo, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XI, 306 p. 205 ilustraciones, 114 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-58309-5 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  Visión por computador  Procesamiento de datos  Sistemas de reconocimiento de patrones  Procesamiento de imágenes  Minería de datos y descubrimiento de conocimientos  Reconocimiento de patrones automatizado  Imágenes por computadora  visión  reconocimiento de patrones y gráficos Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del noveno taller internacional IAPR TC3 sobre redes neuronales artificiales en reconocimiento de patrones, ANNPR 2020, celebrado en Winterthur, Suiza, en septiembre de 2020. La conferencia se llevó a cabo virtualmente debido a la pandemia de COVID-19. Los 22 artículos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 34 presentaciones. Los artículos presentan y analizan las últimas investigaciones en todas las áreas del reconocimiento de patrones basado en redes neuronales y aprendizaje automático. Están organizados en dos secciones: algoritmos y arquitecturas de aprendizaje, y aplicaciones. Nota de contenido: Deep Learning Methods for Image Guidance in Radiation Therapy Intentional Image Similarity Search -- Sttructured (De)composable Representations Trained with Neural Networks -- Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling -- Improving Accuracy and Efficiency of Object Detection Algorithms using Multiscale Feature Aggregation Plugins -- Abstract Echo State Networks -- Minimal Complexity Support Vector Machines -- Named Entity Disambiguation at Scale -- Geometric Attention for Prediction of Differential Properties in 3D Point Clouds -- How (Not) to Measure Bias in Face Recognition Networks.-Feature Extraction: A Time Window Analysis based on the X-ITE Pain Database -- Pain Intensity Recognition - An Analysis of Short-Time Sequences in a Real-World Scenario -- A deep learning approach for efficient registration of dual view mammography -- Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology -- Applications of Generative Adversarial Networks to Dermatologic Imaging -- Typing Plasmids with Distributed Sequence Representation -- KP-YOLO: a modification of YOLO algorithm for the keypoint-based detection of QR Codes -- Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools -- A Hybrid Deep Learning Approach For Forecasting Air Temperature -- Using CNNs to optimize numerical simulations in geotechnical engineering -- Going for 2D or 3D? Investigating various Machine Learning Approaches for Peach Variety Identification -- A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture -- ML-Based Trading Models: An investigation during COVID-19 pandemic crisis -- iNNvestigate-GUI - Explaining Neural Networks Through an Interactive Visualization Tool. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] 9th IAPR TC3 Workshop, ANNPR 2020, Winterthur, Switzerland, September 2–4, 2020, Proceedings [documento electrónico] / Schilling, Frank-Peter, ; Stadelmann, Thilo,  . -  1 ed.  . - [s.l.] : Springer, 2020 . - XI, 306 p. 205 ilustraciones, 114 ilustraciones en color. ISBN : 978-3-030-58309-5 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  Visión por computador  Procesamiento de datos  Sistemas de reconocimiento de patrones  Procesamiento de imágenes  Minería de datos y descubrimiento de conocimientos  Reconocimiento de patrones automatizado  Imágenes por computadora  visión  reconocimiento de patrones y gráficos Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del noveno taller internacional IAPR TC3 sobre redes neuronales artificiales en reconocimiento de patrones, ANNPR 2020, celebrado en Winterthur, Suiza, en septiembre de 2020. La conferencia se llevó a cabo virtualmente debido a la pandemia de COVID-19. Los 22 artículos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 34 presentaciones. Los artículos presentan y analizan las últimas investigaciones en todas las áreas del reconocimiento de patrones basado en redes neuronales y aprendizaje automático. Están organizados en dos secciones: algoritmos y arquitecturas de aprendizaje, y aplicaciones. Nota de contenido: Deep Learning Methods for Image Guidance in Radiation Therapy Intentional Image Similarity Search -- Sttructured (De)composable Representations Trained with Neural Networks -- Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling -- Improving Accuracy and Efficiency of Object Detection Algorithms using Multiscale Feature Aggregation Plugins -- Abstract Echo State Networks -- Minimal Complexity Support Vector Machines -- Named Entity Disambiguation at Scale -- Geometric Attention for Prediction of Differential Properties in 3D Point Clouds -- How (Not) to Measure Bias in Face Recognition Networks.-Feature Extraction: A Time Window Analysis based on the X-ITE Pain Database -- Pain Intensity Recognition - An Analysis of Short-Time Sequences in a Real-World Scenario -- A deep learning approach for efficient registration of dual view mammography -- Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology -- Applications of Generative Adversarial Networks to Dermatologic Imaging -- Typing Plasmids with Distributed Sequence Representation -- KP-YOLO: a modification of YOLO algorithm for the keypoint-based detection of QR Codes -- Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools -- A Hybrid Deep Learning Approach For Forecasting Air Temperature -- Using CNNs to optimize numerical simulations in geotechnical engineering -- Going for 2D or 3D? Investigating various Machine Learning Approaches for Peach Variety Identification -- A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture -- ML-Based Trading Models: An investigation during COVID-19 pandemic crisis -- iNNvestigate-GUI - Explaining Neural Networks Through an Interactive Visualization Tool. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]

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