Variational and Level Set Methods in Image Segmentation [recurso electrónico] / by Amar Mitiche, Ismail Ben Ayed.

Por: Mitiche, Amar [author.]Colaborador(es): Ben Ayed, Ismail [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Springer Topics in Signal Processing ; 5Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: VIII, 192 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642153525Tema(s): Engineering | Computer vision | Engineering | Signal, Image and Speech Processing | Image Processing and Computer VisionFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 621.382 Clasificación LoC:TK5102.9TA1637-1638TK7882.S65Recursos en línea: Libro electrónicoTexto
Contenidos:
Introduction -- Image Segmentation -- Image Models -- Optical Flow Estimation -- Joint Optical Flow Estimation and Segmentation -- Optical Flow 3D segmentation -- Appendix.
En: Springer eBooksResumen: 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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Colección de Libros Electrónicos TK5102.9 (Browse shelf(Abre debajo)) 1 No para préstamo 375007-2001

Introduction -- Image Segmentation -- Image Models -- Optical Flow Estimation -- Joint Optical Flow Estimation and Segmentation -- Optical Flow 3D segmentation -- Appendix.

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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