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Hardcover. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Add to basketHardcover. Condition: Brand New. 250 pages. 9.00x6.00x0.63 inches. In Stock.
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High-Order Models in Semantic Image Segmentation
Language: English
Published by Elsevier Science Publishing Co Inc, 2023
ISBN 10: 0128053208 ISBN 13: 9780128053201
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Add to basketPaperback. Condition: Brand New. 275 pages. 9.25x7.50x9.25 inches. In Stock.
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Less-Supervised Segmentation with CNNs: Scenarios, Models and Optimization
Language: English
Published by Elsevier Science & Technology, 2025
ISBN 10: 0323956742 ISBN 13: 9780323956741
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Variational and Level Set Methods in Image Segmentation (Springer Topics in Signal Processing, 5)
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Variational and Level Set Methods in Image Segmentation (Springer Topics in Signal Processing, 5)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Condition: New. pp. viii + 192.
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Add to basketPaperback. Condition: Brand New. 2011 edition. 250 pages. 9.25x6.10x0.47 inches. In Stock.
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Variational and Level Set Methods in Image Segmentation
Language: English
Published by Springer Berlin Heidelberg, 2012
ISBN 10: 3642265626 ISBN 13: 9783642265624
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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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Variational and Level Set Methods in Image Segmentation
Language: English
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642153518 ISBN 13: 9783642153518
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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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Less-Supervised Segmentation with CNNs (Paperback)
Language: English
Published by Elsevier Science & Technology, Oxford, 2025
ISBN 10: 0323956742 ISBN 13: 9780323956741
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Add to basketPaperback. Condition: new. Paperback. Less-Supervised Segmentation with CNNs: Scenarios, Models and Optimization reviews recent progress in deep learning for image segmentation under scenarios with limited supervision, with a focus on medical imaging. The book presents main approaches and state-of-the-art models and includes a broad array of applications in medical image segmentation, including healthcare, oncology, cardiology and neuroimaging. A key objective is to make this mathematical subject accessible to a broad engineering and computing audience by using a large number of intuitive graphical illustrations. The emphasis is on giving conceptual understanding of the methods to foster easier learning.This book is highly suitable for researchers and graduate students in computer vision, machine learning and medical imaging. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Variational and Level Set Methods in Image Segmentation
Language: English
Published by Springer Berlin Heidelberg Dez 2012, 2012
ISBN 10: 3642265626 ISBN 13: 9783642265624
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 200 pp. Englisch.
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Variational and Level Set Methods in Image Segmentation
Language: English
Published by Springer Berlin Heidelberg Okt 2010, 2010
ISBN 10: 3642153518 ISBN 13: 9783642153518
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 200 pp. Englisch.
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Variational and Level Set Methods in Image Segmentation
Language: English
Published by Springer Berlin Heidelberg, 2012
ISBN 10: 3642265626 ISBN 13: 9783642265624
US$ 145.04
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Useful applications in remote sensing, medicineUseful applications in robotics, database searchUseful applications in securityImage segmentation consists of dividing an image domain into disjoint regions according to a characterizat.
