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"This book reviews the algorithms for processing geometric data, with a practical focus on important techniques not covered by traditional courses on computer vision and computer graphics. Features, presents an overview of the underlying mathematical theory, covering vector spaces, metric space, affine spaces, differential geometry, and finite difference methods for derivatives and differential equations, reviews geometry representations, including polygonal meshes, splines, and subdivision surfaces, examines techniques for computing curvature from polygonal meshes, describes algorithms for mesh smoothing, mesh parametrization, and mesh optimization and simplification, discusses point location databases and convex hulls of point sets, investigates the reconstruction of triangle meshes from point clouds, including methods for registration of point clouds and surface reconstruction, provides additional material at a supplementary website, and includes self-study exercises throughout the text."

"This book is that explicitly focuses on the presentation of the most important digital geometry algorithms. Each chapter provides a brief survey on a major research area related to the general volume theme, description and analysis of related fundamental algorithms, as well as new original contributions by the authors."

"This book presents a comprehensive overview of medical image analysis. Practical in approach, the text is uniquely structured by potential applications. Features : presents learning objectives, exercises and concluding remarks in each chapter, in addition to a glossary of abbreviations; describes a range of common imaging techniques, reconstruction techniques and image artefacts, discusses the archival and transfer of images, including the HL7 and DICOM standards, presents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothing, examines various feature detection and segmentation techniques, together with methods for computing a registration or normalisation transformation, explores object detection, as well as classification based on segment attributes such as shape and appearance, reviews the validation of an analysis method, includes appendices on Markov random field optimization, variational calculus and principal component analysis."