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Variational and Level Set Methods in Image Segmentation (Hardcover, 2011)
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Variational and Level Set Methods in Image Segmentation (Hardcover, 2011)
Series: Springer Topics in Signal Processing, 5
Expected to ship within 10 - 15 working days
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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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