Landmark Based Image Analysis Using by Rohr Karl (10 results)
Language: English
Published by Springer 2001
Series: Computational Imaging and Vision, Book 13 of 20. Book 13 of 20 - Computational Imaging and Vision
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Published by Springer 2010
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Language: English
Published by Springer 2001
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Language: English
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More imagesLanguage: English
Published by Springer 2010
Series: Computational Imaging and Vision, Book 13 of 20. Book 13 of 20 - Computational Imaging and Vision
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Taschenbuch. Condition: Neu. Landmark-Based Image Analysis | Using Geometric and Intensity Models | Karl Rohr | Taschenbuch | xiv | Englisch | 2010 | Springer | EAN 9789048156306 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: pr…eigu.
Language: English
Published by Springer Netherlands 2001
Series: Computational Imaging and Vision, Book 13 of 20. Book 13 of 20 - Computational Imaging and Vision
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Published by Springer, Springer 2010
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Landmarks are preferred image features for a variety of computer vision tasks such as image mensuration, registration, camera calibration, motion analysis, 3D scene reconstruction, and object recognition. Main advantages of using landmarks are robu…stness w. r. t. lightning conditions and other radiometric vari ations as well as the ability to cope with large displacements in registration or motion analysis tasks. Also, landmark-based approaches are in general com putationally efficient, particularly when using point landmarks. Note, that the term landmark comprises both artificial and natural landmarks. Examples are comers or other characteristic points in video images, ground control points in aerial images, anatomical landmarks in medical images, prominent facial points used for biometric verification, markers at human joints used for motion capture in virtual reality applications, or in- and outdoor landmarks used for autonomous navigation of robots. This book covers the extraction oflandmarks from images as well as the use of these features for elastic image registration. Our emphasis is onmodel-based approaches, i. e. on the use of explicitly represented knowledge in image analy sis. We principally distinguish between geometric models describing the shape of objects (typically their contours) and intensity models, which directly repre sent the image intensities, i. e. ,the appearance of objects. Based on these classes of models we develop algorithms and methods for analyzing multimodality im ages such as traditional 20 video images or 3D medical tomographic images.
Language: English
Published by Springer Feb 2001 2001
Series: Computational Imaging and Vision, Book 13 of 20. Book 13 of 20 - Computational Imaging and Vision
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. Neuware - Landmarks are preferred image features for a variety of computer vision tasks such as image mensuration, registration, camera calibration, motion analysis, 3D scene reconstruction, and object recognition. Main advantages of using landmarks are robustness w. r. t. lightning conditions and other rad…iometric vari ations as well as the ability to cope with large displacements in registration or motion analysis tasks. Also, landmark-based approaches are in general com putationally efficient, particularly when using point landmarks. Note, that the term landmark comprises both artificial and natural landmarks. Examples are comers or other characteristic points in video images, ground control points in aerial images, anatomical landmarks in medical images, prominent facial points used for biometric verification, markers at human joints used for motion capture in virtual reality applications, or in- and outdoor landmarks used for autonomous navigation of robots. This book covers the extraction oflandmarks from images as well as the use of these features for elastic image registration. Our emphasis is onmodel-based approaches, i. e. on the use of explicitly represented knowledge in image analy sis. We principally distinguish between geometric models describing the shape of objects (typically their contours) and intensity models, which directly repre sent the image intensities, i. e. ,the appearance of objects. Based on these classes of models we develop algorithms and methods for analyzing multimodality im ages such as traditional 20 video images or 3D medical tomographic images.
