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Springer Riemannian Computing in Computer Vision - Springer

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Description

Riemannian Computing in Computer Vision is a comprehensive edited volume from Springer that explores geometric computational methods and statistical inference techniques applied to modern computer vision challenges. Written by leading researchers in the field, this book covers practical applications including face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. The volume addresses complex mathematical entities that require geometric analysis, such as rotation matrices for camera motion modelling and geometric representations used in action recognition. Whether you are a computer vision researcher, machine learning engineer, or academic studying advanced computational geometry, this treatise provides essential theoretical foundations and practical methodologies for implementing Riemannian geometric approaches in real-world vision problems.

Key Features

Covers Riemannian geometric computations applied to face recognition, activity recognition, and object detection using structured mathematical approaches.

Includes contributions from leading computer vision researchers presenting cutting-edge applications of geometric methods in biomedical image analysis.

Addresses rotation matrices and geometric representations essential for modelling camera motion and structure-from-motion problems.

Provides comprehensive statistical inference techniques paired with Riemannian geometric analysis for advanced vision applications.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
18 November 2015
Listed Since
09 July 2015

Barcode

No barcode data available

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