The goal of image interpretation is to convert raw image data into me- ingful information. Images are often interpreted manually. In medicine, for example, a radiologist looks at a medical image, interprets it, and tra- lates the data into a clinically useful form. Manual image interpretation is, however, a time-consuming, error-prone, and subjective process that often requires specialist knowledge. Automated methods that promise fast and - jective image interpretation have therefore stirred up much interest and have become a signi?cant area of research activity. Early work on automated interpretation used low-level operations such as edge detection and region growing to label objects in images. These can p- ducereasonableresultsonsimpleimages,butthepresenceofnoise,occlusion, andstructuralcomplexity oftenleadstoerroneouslabelling. Furthermore,- belling an object is often only the ?rst step of the interpretation process. In order to perform higher-level analysis, a priori information must be incor- rated into the interpretation process. A convenient way of achieving this is to use a ?exible model to encode information such as the expected size, shape, appearance, and position of objects in an image. The use of ?exible models was popularized by the active contour model, or ‘snake’ [98]. A snake deforms so as to match image evidence (e.g., edges) whilst ensuring that it satis?es structural constraints. However, a snake lacks speci?city as it has little knowledge of the domain, limiting its value in image interpretation.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Deformable shape models have wide application in computer vision and biomedical image analysis. This book addresses a key issue in shape modelling: establishment of a meaningful correspondence between a set of shapes. Full implementation details are provided.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The goal of image interpretation is to convert raw image data into me- ingful information. Images are often interpreted manually. In medicine, for example, a radiologist looks at a medical image, interprets it, and tra- lates the data into a clinically useful form. Manual image interpretation is, however, a time-consuming, error-prone, and subjective process that often requires specialist knowledge. Automated methods that promise fast and - jective image interpretation have therefore stirred up much interest and have become a signi cant area of research activity. Early work on automated interpretation used low-level operations such as edge detection and region growing to label objects in images. These can p- ducereasonableresultsonsimpleimages,butthepresenceofnoise,occlusion, andstructuralcomplexity oftenleadstoerroneouslabelling. Furthermore,- belling an object is often only the rst step of the interpretation process. In order to perform higher-level analysis, a priori information must be incor- rated into the interpretation process. A convenient way of achieving this is to use a exible model to encode information such as the expected size, shape, appearance, and position of objects in an image. The use of exible models was popularized by the active contour model, or 'snake' [98]. A snake deforms so as to match image evidence (e.g., edges) whilst ensuring that it satis es structural constraints. However, a snake lacks speci city as it has little knowledge of the domain, limiting its value in image interpretation. 316 pp. Englisch. N° de réf. du vendeur 9781447160427
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Addresses one of the key issues in shape modelling: that of establishing a meaningful correspondence between a set of shapesUses a novel approach to establishing correspondence by casting model-building as an optimisation problem Includes p. N° de réf. du vendeur 11465379
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The goal of image interpretation is to convert raw image data into me- ingful information. Images are often interpreted manually. In medicine, for example, a radiologist looks at a medical image, interprets it, and tra- lates the data into a clinically useful form. Manual image interpretation is, however, a time-consuming, error-prone, and subjective process that often requires specialist knowledge. Automated methods that promise fast and - jective image interpretation have therefore stirred up much interest and have become a signi cant area of research activity. Early work on automated interpretation used low-level operations such as edge detection and region growing to label objects in images. These can p- ducereasonableresultsonsimpleimages,butthepresenceofnoise,occlusion, andstructuralcomplexity oftenleadstoerroneouslabelling. Furthermore,- belling an object is often only the rst step of the interpretation process. In order to perform higher-level analysis, a priori information must be incor- rated into the interpretation process. A convenient way of achieving this is to use a exible model to encode information such as the expected size, shape, appearance, and position of objects in an image. The use of exible models was popularized by the active contour model, or 'snake' [98]. A snake deforms so as to match image evidence (e.g., edges) whilst ensuring that it satis es structural constraints. However, a snake lacks speci city as it has little knowledge of the domain, limiting its value in image interpretation. N° de réf. du vendeur 9781447160427
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Taschenbuch. Etat : Neu. Statistical Models of Shape | Optimisation and Evaluation | Rhodri Davies (u. a.) | Taschenbuch | xii | Englisch | 2014 | Springer | EAN 9781447160427 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 105029051
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The goal of image interpretation is to convert raw image data into me- ingful information. Images are often interpreted manually. In medicine, for example, a radiologist looks at a medical image, interprets it, and tra- lates the data into a clinically useful form. Manual image interpretation is, however, a time-consuming, error-prone, and subjective process that often requires specialist knowledge. Automated methods that promise fast and - jective image interpretation have therefore stirred up much interest and have become a signi cant area of research activity. Early work on automated interpretation used low-level operations such as edge detection and region growing to label objects in images. These can p- ducereasonableresultsonsimpleimages,butthepresenceofnoise,occlusion, andstructuralcomplexity oftenleadstoerroneouslabelling. Furthermore,- belling an object is often only the rst step of the interpretation process. In order to perform higher-level analysis, a priori information must be incor- rated into the interpretation process. A convenient way of achieving this is to use a exible model to encode information such as the expected size, shape, appearance, and position of objects in an image. The use of exible models was popularized by the active contour model, or ¿snake¿ [98]. A snake deforms so as to match image evidence (e.g., edges) whilst ensuring that it satis es structural constraints. However, a snake lacks speci city as it has little knowledge of the domain, limiting its value in image interpretation.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 316 pp. Englisch. N° de réf. du vendeur 9781447160427
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