Name: Riemannian Geometric Statistics in Medical Image Analysis (The Elsevier and Miccai Society) (en Inglés)
Price: 131.58 USD
Availability: InStock
ISBN: 9780128147252

portada Riemannian Geometric Statistics in Medical Image Analysis (The Elsevier and Miccai Society) (en Inglés)

Formato

Libro Físico

Editorial

Academic Press

Año

2019

Idioma

Inglés

N° páginas

636

Encuadernación

Tapa Blanda

ISBN13

9780128147252

N° edición

Categorías

Reconocimiento de formas
Visión por ordenador

Riemannian Geometric Statistics in Medical Image Analysis (The Elsevier and Miccai Society) (en Inglés)

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Reseña del libro "Riemannian Geometric Statistics in Medical Image Analysis (The Elsevier and Miccai Society) (en Inglés)"

Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Content includes: • The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs • Applications of statistics on manifolds and shape spaces in medical image computing • Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science. • A complete reference covering both the foundations and state-of-the-art methods • Edited and authored by leading researchers in the field • Contains theory, examples, applications, and algorithms • Gives an overview of current research challenges and future applications