Geometric Level Set Methods in Imaging, Vision, and Graphics / Edition 1

by Stanley Osher, Nikos Paragios

Overview

Introduction Imageprocessing,computervisionandcomputergraphicsarenowestablished - search areas. Pattern recognition and artificial intelligence were the origins of the explorationof thespace ofimages.Simplistic digitaltechniquesusedatthe beg- ning of 60’s for gray image processing operations have been now replaced with a complex mathematical framework that aims to exploit and understand images in two and three dimensions. Advances in computing power continue to make the use and processing of visual information an important part of our lives. The evolution of these techniques was a natural outcome of the need to process an emerging informationspace, the space of natural images. Images in space and time are now a critical part of many human activities. First, pictures and now video streams were used to eternalize small and significant moments of our life. Entertainment including movies, TV-programs and video games are part of our every-day life where capturing, editing, understanding and transmitting images are issues to be dealt with. The medical sector is also a major area for the use of images. The evolution of the acquisition devices led to new ways of capturing information, not visible by the human eye. Medical imaging is probably the most established market for processing visual information[405]. Visualization of c- plex structures and automated processing towards computer aided diagnosis is used more and more by the physicians in the diagnostic process. Safety and se- rity are also important areas where images and video play a significant role [432].

Product Details

ISBN-13:	9781441930231
Publisher:	Springer New York
Publication date:	12/14/2011
Edition description:	Softcover reprint of the original 1st ed. 2003
Pages:	513
Product dimensions:	6.10(w) x 9.25(h) x 0.04(d)

Table of Contents

Level Set Methods & Lagrangian Approaches.- Level Set Methods.- Deformable Models: Classic, Topology-Adaptive and Generalized Formulations.- Edge Detection & Boundary Extraction.- Fast Methods for Implicit Active Contour Models.- Fast Edge Integration.- Variational Snake Theory.- Scalar & Vector Image Reconstruction, Restoration.- Multiplicative Denoising and Deblurring: Theory and Algorithms.- Total Variation Minimization for Scalar/Vector Regularization.- Morphological Global Reconstruction and Levelings: Lattice and PDE Approaches.- Grouping.- Fast Marching Techniques for Visual Grouping & Segmentation.- Multiphase Object Detection and Image Segmentation.- Adaptive Segmentation of Vector Valued Images.- Mumford-Shah for Segmentation and Stereo.- Knowledge-based Segmentation & Registration.- Shape Analysis towards Model-based Segmentation.- Joint Image Registration and Segmentation.- Image Alignment.- Motion Analysis.- Variational Principles in Optical Flow Estimation and Tracking.- Region Matching and Tracking under Deformations or Occlusions.- Computational Stereo & Implicit Surfaces.- Computational Stereo: A Variational Method.- Visualization, Analysis and Shape Reconstruction of Sparse Data.- Variational Problems and Partial Differential Equations on Implicit Surfaces: Bye Bye Triangulated Surfaces?.- Medical Image Analysis.- Knowledge-Based Segmentation of Medical Images.- Topology Preserving Geometric Deformable Models for Brain Reconstruction.- Simulations & Graphics.- Editing Geometric Models.- Simulating Natural Phenomena.