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.
Автор: Jia He; Chang-Su Kim; C.-C. Jay Kuo Название: Interactive Segmentation Techniques ISBN: 9814451592 ISBN-13(EAN): 9789814451598 Издательство: Springer Рейтинг: Цена: 9141.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will first introduce classic graph-cut segmentation algorithms and then discuss state-of-the-art techniques, including graph matching methods, region merging and label propagation, clustering methods, and segmentation methods based on edge detection.
Автор: Siddhartha Bhattacharyya; Paramartha Dutta; Sourav Название: Hybrid Soft Computing for Image Segmentation ISBN: 3319472224 ISBN-13(EAN): 9783319472225 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization.The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.
Автор: Maria A. Zuluaga; Kanwal Bhatia; Bernhard Kainz; M Название: Reconstruction, Segmentation, and Analysis of Medical Images ISBN: 3319522795 ISBN-13(EAN): 9783319522791 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Registration.- Reconstruction.- Deep learning for heart segmentation.- Discrete optimization and probabilistic intensity modeling.- Atlas-based strategies.- Random forests.
Автор: Jia Li; Robert M. Gray Название: Image Segmentation and Compression Using Hidden Markov Models ISBN: 1461370272 ISBN-13(EAN): 9781461370277 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Conventional block-based segmentation algorithms classify each block separately, assuming independence of feature vectors. Image Segmentation and Compression Using Hidden Markov Models presents a new algorithm that models the statistical dependence among image blocks by two dimensional hidden Markov models (HMMs).
Описание: This book discusses email spam detection and its challenges such as text classification and categorization.
Автор: Diego Oliva; Mohamed Abd Elaziz; Salvador Hinojosa Название: Metaheuristic Algorithms for Image Segmentation: Theory and Applications ISBN: 3030129306 ISBN-13(EAN): 9783030129309 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designed to solve complex optimization problems increases.
This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.
Автор: Bir Bhanu; Sungkee Lee Название: Genetic Learning for Adaptive Image Segmentation ISBN: 1461361982 ISBN-13(EAN): 9781461361985 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Genetic Learning for Adaptive Image Segmentation presents the first closed-loop image segmentation system that incorporates genetic and other algorithms to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions, such as time of day, time of year, weather, etc.
Автор: Bir Bhanu; Sungkee Lee Название: Genetic Learning for Adaptive Image Segmentation ISBN: 0792394917 ISBN-13(EAN): 9780792394914 Издательство: Springer Рейтинг: Цена: 27944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Genetic Learning for Adaptive Image Segmentation presents the first closed-loop image segmentation system that incorporates genetic and other algorithms to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions, such as time of day, time of year, weather, etc.
Автор: Dagmar Kainmueller Название: Deformable Meshes for Medical Image Segmentation ISBN: 3658070145 ISBN-13(EAN): 9783658070144 Издательство: Springer Рейтинг: Цена: 9141.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: ГЇВїВЅ Segmentation of anatomical structures in medical image data is an essential task in clinical practice. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatomical structures, together with thorough evaluations of segmentation accuracy on clinical image data.
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: de Sourav, Bhattacharyya Siddhartha, Chakraborty Susanta Название: Hybrid Soft Computing for Multilevel Image and Data Segmentation ISBN: 3319837583 ISBN-13(EAN): 9783319837581 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
