Variational and Level Set Methods in Image Segmentation (Springer Topics in Signal Processing, 5) - Hardcover

Mitiche, Amar; Ben Ayed, Ismail

 
9783642153518: Variational and Level Set Methods in Image Segmentation (Springer Topics in Signal Processing, 5)
Hardcover
ISBN 10: 3642153518 ISBN 13: 9783642153518
Publisher: Springer, 2010

Synopsis

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.

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From the Back Cover

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.

"About this title" may belong to another edition of this title.

Publisher
Springer
Publication date
2010
Language
English
ISBN 10 
3642153518
ISBN 13 
9783642153518
Binding
Hardcover
Number of pages
200

Other Popular Editions of the Same Title

9783642265624: Variational and Level Set Methods in Image Segmentation (Springer Topics in Signal Processing, 5)

Featured Edition

ISBN 10:  3642265626 ISBN 13:  9783642265624
Publisher: Springer, 2012
Softcover