Variational and Level Set Methods in Image Segmentation

  • Amar Mitiche
  • Ismail Ben Ayed

Part of the Springer Topics in Signal Processing book series (STSP, volume 5)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Amar Mitiche, Ismail Ben Ayed
    Pages 1-13
  3. Amar Mitiche, Ismail Ben Ayed
    Pages 15-31
  4. Amar Mitiche, Ismail Ben Ayed
    Pages 33-58
  5. Amar Mitiche, Ismail Ben Ayed
    Pages 59-81
  6. Amar Mitiche, Ismail Ben Ayed
    Pages 83-122
  7. Amar Mitiche, Ismail Ben Ayed
    Pages 123-137
  8. Amar Mitiche, Ismail Ben Ayed
    Pages 139-160
  9. Amar Mitiche, Ismail Ben Ayed
    Pages 161-180
  10. Amar Mitiche, Ismail Ben Ayed
    Pages 181-188
  11. Back Matter
    Pages 189-190

About this book

Introduction

Image segmentation consists of dividing an image domain into disjoint regions according to a characterization of the image within or in-between the regions. Therefore, segmenting an image is to divide its domain into relevant components. The efficient solution of the key problems in image segmentation promises to enable a rich array of useful applications. The current major application areas include robotics, medical image analysis, remote sensing, scene understanding, and image database retrieval. The subject of this book is image segmentation by variational methods with a focus on formulations which use closed regular plane curves to define the segmentation regions and on a level set implementation of the corresponding active curve evolution algorithms. Each method is developed from an objective functional which embeds constraints on both the image domain partition of the segmentation and the image data within or in-between the partition regions. The necessary conditions to optimize the objective functional are then derived and solved numerically. The book covers, within the active curve and level set formalism, the basic two-region segmentation methods, multiregion extensions, region merging, image modeling, and motion based segmentation. To treat various important classes of images, modeling investigates several parametric distributions such as the Gaussian, Gamma, Weibull, and Wishart. It also investigates non-parametric models. In motion segmentation, both optical flow and the movement of real three-dimensional objects are studied.

Keywords

Flow Estimation Image Models Optical Flow 3D segmentaion

Authors and affiliations

  • Amar Mitiche
    • 1
  • Ismail Ben Ayed
    • 2
  1. 1., INRS Energie, Matériaux et TélécomUniversité de QuebecMontrealCanada
  2. 2.Inst. National de la Recherche, Scientifique (INRS)Université de QuebecMontrealCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-15352-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-15351-8
  • Online ISBN 978-3-642-15352-5
  • Series Print ISSN 1866-2609
  • Series Online ISSN 1866-2617
  • About this book