Автор: Hati Название: Image Co-segmentation ISBN: 9811985693 ISBN-13(EAN): 9789811985690 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Поставка под заказ.
Описание: This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.
Описание: Semantic Video Object Segmentation for Content-Based Multimedia Applications provides a thorough review of state-of-the-art techniques as well as describing several novel ideas and algorithms for semantic object extraction from image sequences.
Автор: Zhou,S. Kevin Название: Medical Image Recognition, Segmentation And Parsing ISBN: 0128025816 ISBN-13(EAN): 9780128025819 Издательство: Elsevier Science Рейтинг: Цена: 18022.00 р. Наличие на складе: Поставка под заказ.
Автор: King Ngi Ngan; Hongliang Li Название: Video Segmentation and Its Applications ISBN: 1489994661 ISBN-13(EAN): 9781489994660 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the latest advances in video segmentation and analysis techniques and covers the theoretical approaches, real applications and methods developed in the computer vision and video analysis community.
Автор: Alireza Bab-Hadiashar; David Suter Название: Data Segmentation and Model Selection for Computer Vision ISBN: 1468495089 ISBN-13(EAN): 9781468495089 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This edited volume explores several issues relating to parametric segmentation including robust operations, model selection criteria and automatic model selection, plus 2D and 3D scene segmentation.
Описание: This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020.
Автор: David Vernon Название: Fourier Vision ISBN: 1461355419 ISBN-13(EAN): 9781461355410 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Exploiting the relative motion between figure and ground, this technique deals explicitly with the separation of additive signals and makes no assumptions about the spatial or spectral content of the images, with segmentation being carried out phasor by phasor in the Fourier domain.
Автор: Hati Название: Image Co-segmentation ISBN: 9811985723 ISBN-13(EAN): 9789811985720 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.
Описание: Multi-atlas segmentation of the aorta from 4D flow MRI: comparison of several fusion strategie.- Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data.- Coronary Artery Centerline Refinement using GCN Trained with Synthetic Data.- Novel imaging biomarkers to evaluate heart dysfunction post-chemotherapy: a preclinical MRI feasibility study.- A bi-atrial statistical shape model as a basis to classify left atrial enlargement from simulated and clinical 12-lead ECGs.- Vessel Extraction and Analysis of Aortic Dissection.- The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images.- Characterizing myocardial ischemia and reperfusion patterns with hierarchical manifold learning.- Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders.- Improved AI-based Segmentation of Apical and Basal Slices from Clinical Cine CMR.- Mesh Convolutional Neural Networks for Wall Shear Stress Estimation in 3D Artery Models.- Hierarchical multi-modality prediction model to assess obesity-related remodelling.- Neural Angular Plaque Characterization: Automated Quantification of Polar Distributionfor Plaque Composition.- Simultaneous Segmentation and Motion Estimation of Left Ventricular Myocardium in 3D Echocardiography using Multi-task Learning.- Statistical shape analysis of the tricuspid valve in hypoplastic left heart syndrome.- An Unsupervised 3D Recurrent Neural Networkfor Slice Misalignment Correction in CardiacMR Imaging.- Unsupervised Multi-Modality RegistrationNetwork based on Spatially Encoded Gradient Information.- In-silico analysis of device-related thrombosis for different left atrial appendage occluder settings.- Valve flattening with functional biomarkers for the assessment of mitral valve repair.- Multi-modality cardiac segmentation via mixing domains for unsupervised adaptation.- Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction.- Cross-domain Artefact Correction of Cardiac MRI.- Detection and Classification of Coronary Artery Plaques in Coronary Computed Tomography Angiography Using 3D CNN.- Predicting 3D Cardiac Deformations With Point Cloud Autoencoders.- Influence of morphometric and mechanical factors in thoracic aorta finite element modeling.- Right Ventricle Segmentation via Registration and Multi-input Modalities in Cardiac Magnetic Resonance Imaging from Multi-Disease, Multi-View and Multi-Center.- Using MRI-specific Data Augmentation to Enhance the Segmentation of Right Ventricle in Multi-disease, Multi-center and Multi-view Cardiac MRI.- Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition.- Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation.- Multi-view SA-LA Net: A framework for simultaneous segmentation of RV on multi-view cardiac MR Images.- Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model.- Deformable Bayesian Convolutional Networks for Disease-Robust Cardiac MRI Segmentation.- Consistency based Co-Segmentation for Multi-View Cardiac MRI using Vision Transformer.- Refined Deep Layer Aggregation for Multi-Disease, Multi-View & Multi-Center Cardiac MR Segmentation.- A Multi-View Cross-Over Attention U-Net Cascade With Fourier Domain Adaptation For Multi-Domain Cardiac MRI Segmentation.- Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI using Efficient Late-Ensemble Deep Learning Approach.- Automated Segmentation of the Right Ventricle from Magnetic Resonance Imaging Using Deep Convolutional Neural Networks.- 3D right ventricle reconstruction from 2D U-Net segmentation of sparse short-axis and 4-chamber cardiac cine MRI views.- Late Fusion U-Net with GAN-based Augmentation for Generalizable Cardiac MRI Segmentation.- Using Out-of-Distribution Detection for Model Refinement in Cardiac Im
Автор: S. Kamaledin Setarehdan; Sameer Singh Название: Advanced Algorithmic Approaches to Medical Image Segmentation ISBN: 1447110439 ISBN-13(EAN): 9781447110439 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Medical imaging is an important topic and plays a key role in robust diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound.
Автор: Branka Stojanovi?; Oge Marques; Aleksandar Ne?kovi Название: Segmentation and Separation of Overlapped Latent Fingerprints ISBN: 3030233634 ISBN-13(EAN): 9783030233631 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This Springerbrief presents an overview of problems and technologies behind segmentation and separation of overlapped latent fingerprints, which are two fundamental steps in the context of fingerprint matching systems. It addresses five main aspects: (1) the need for overlapped latent fingerprint segmentation and separation in the context of fingerprint verification systems; (2) the different datasets available for research on overlapped latent fingerprints; (3) selected algorithms and techniques for segmentation of overlapped latent fingerprints; (4) selected algorithms and techniques for separation of overlapped latent fingerprints; and (5) the use of deep learning techniques for segmentation and separation of overlapped latent fingerprints.
By offering a structured overview of the most important approaches currently available, putting them in perspective, and suggesting numerous resources for further exploration, this book gives its readers a clear path for learning new topics and engaging in related research. Written from a technical perspective, and yet using language and terminology accessible to non-experts, it describes the technologies, introduces relevant datasets, highlights the most important research results in each area, and outlines the most challenging open research questions.
This Springerbrief targets researchers, professionals and advanced-level students studying and working in computer science, who are interested in the field of fingerprint matching and biometrics. Readers who want to deepen their understanding of specific topics will find more than one hundred references to additional sources of related information.
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