Geometric Level Set Methods in Imaging, Vision, and Graphics

Stanley Osher, Nikos Paragios

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English

Springer-Verlag New York Inc.
14 December 2011

Numerical analysis; Imaging systems & technology; Graphics programming; Pattern recognition; Image processing

Here is, for the first time, a book that clearly explains and applies new level set methods to problems and applications in computer vision, graphics, and imaging. It is an essential compilation of survey chapters from the leading researchers in the field. The applications of the methods are emphasized.

Edited by: Stanley Osher, Nikos Paragios
Imprint: Springer-Verlag New York Inc.
Country of Publication: United States
Edition: Softcover reprint of the original 1st ed. 2003
Dimensions: Height: 235mm, Width: 155mm, Spine: 27mm
Weight: 831g
ISBN: 9781441930231
ISBN 10: 144193023X
Pages: 513
Publication Date: 14 December 2011
Audience: Professional and scholarly , Professional and scholarly , Undergraduate , Further / Higher Education
Format: Paperback
Publisher's Status: Active

* Level set methods * Deformable models * Fast methods for implicit active contour models * Fast edge integration * Variational snake theory * Multiplicative denoising and deblurring * Total varation minimization for scalar/vector regularization * Morphological global reconstruction and levelings * Fast marching techniques for visual grouping and segmentation * Multiphase object detection and image segmentation * Adaptive segmentation of vector-valued images * Mumford-Shah for segmentation and stereo * Shape analysis toward model-based segmentation * Joint image registration and segmentation * Image alignment * Variational principles in optical flow estimation and tracking * Region matching and tracking under deformations or occlusions * Computational stereo * Visualization, analysis and shape reconstruction of sparse data * Variational problems and partial differential equations on implicit surfaces * Knowledge-based segmentation of medical images * Topology preserving geometric deformable models for brain reconstruction * Editing geometric models * Simulating natural phenomena