Variational and Level Set Methods in Image Segmentation [electronic resource] / by Amar Mitiche, Ismail Ben Ayed.
Tipo de material: TextoSeries Springer Topics in Signal Processing; -5Descripción: VIII, 192 p. online resourceISBN: 9783642153525 99783642153525Tema(s): Engineering | Engineering | COMPUTADOR VISIÓN | IMAGE PROCESSING AND COMPUTER VISION | SIGNAL, IMAGE AND SPEECH PROCESSINGClasificación CDD: 621.382 Recursos en línea: ir a documentoTipo de ítem | Ubicación actual | Colección | Signatura | Info Vol | Copia número | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
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DOCUMENTOS DIGITALES | Biblioteca Jorge Álvarez Lleras | Digital | 621.382 223 (Navegar estantería) | Ej. 1 | 1 | Disponible | D000772 |
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621.382 223 Digital Filters | 621.382 223 Voice over IP Networks | 621.382 223 Entropy Coders of the H.264 /AVC Standard | 621.382 223 Variational and Level Set Methods in Image Segmentation | 621.382 223 Digital Signal Processing | 621.382 223 The Emerging Domain of Cooperating Objects | 621.382 223 Speech Spectrum Analysis |
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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