Artículos relacionados a Bayesian Modeling of Uncertainty in Low-Level Vision:...
Bayesian Modeling of Uncertainty in Low-Level Vision: 79 (The Springer International Series in Engineering and Computer Science) - Tapa blanda
Sinopsis
Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski’s Ph.D. dissertation at Carnegie Mellon University, presents a Bayesian model for representing and processing uncertainty in low level vision. Recently, probabilistic models have been proposed and used in vision. Sze liski’s method has a few distinguishing features that make this monograph im portant and attractive. First, he presents a systematic Bayesian probabilistic estimation framework in which we can define and compute the prior model, the sensor model, and the posterior model. Second, his method represents and computes explicitly not only the best estimates but also the level of uncertainty of those estimates using second order statistics, i.e., the variance and covariance. Third, the algorithms developed are computationally tractable for dense fields, such as depth maps constructed from stereo or range finder data, rather than just sparse data sets. Finally, Szeliski demonstrates successful applications of the method to several real world problems, including the generation of fractal surfaces, motion estimation without correspondence using sparse range data, and incremental depth from motion.
"Sinopsis" puede pertenecer a otra edición de este libro.
Reseña del editor
Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski's Ph.D. dissertation at Carnegie Mellon University, presents a Bayesian model for representing and processing uncertainty in low level vision. Recently, probabilistic models have been proposed and used in vision. Sze liski's method has a few distinguishing features that make this monograph im portant and attractive. First, he presents a systematic Bayesian probabilistic estimation framework in which we can define and compute the prior model, the sensor model, and the posterior model. Second, his method represents and computes explicitly not only the best estimates but also the level of uncertainty of those estimates using second order statistics, i.e., the variance and covariance. Third, the algorithms developed are computationally tractable for dense fields, such as depth maps constructed from stereo or range finder data, rather than just sparse data sets. Finally, Szeliski demonstrates successful applications of the method to several real world problems, including the generation of fractal surfaces, motion estimation without correspondence using sparse range data, and incremental depth from motion.
"Sobre este título" puede pertenecer a otra edición de este libro.
EUR 175,02
EUR 28,70 gastos de envío desde Reino Unido a Estados Unidos de America
Destinos, gastos y plazos de envíoComprar nuevo
Ver este artículo
EUR 102,69
EUR 2,28 gastos de envío en Estados Unidos de America
Destinos, gastos y plazos de envíoOtras ediciones populares con el mismo título
Resultados de la búsqueda para Bayesian Modeling of Uncertainty in Low-Level Vision:...
Imagen del vendedor
Bayesian Modeling of Uncertainty in Low-level Vision
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 20196201-n
Cantidad disponible: 15 disponibles
Imagen de archivo
Bayesian Modeling of Uncertainty in Low-Level Vision (Paperback)
Publicado por
Springer-Verlag New York Inc., New York, NY, 2011
ISBN 10: 1461289041
ISBN 13: 9781461289043
Nuevo
Paperback
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: new. Paperback. Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski's Ph.D. dissertation at Carnegie Mellon University, presents a Bayesian model for representing and processing uncertainty in low level vision. Recently, probabilistic models have been proposed and used in vision. Sze liski's method has a few distinguishing features that make this monograph im portant and attractive. First, he presents a systematic Bayesian probabilistic estimation framework in which we can define and compute the prior model, the sensor model, and the posterior model. Second, his method represents and computes explicitly not only the best estimates but also the level of uncertainty of those estimates using second order statistics, i.e., the variance and covariance. Third, the algorithms developed are computationally tractable for dense fields, such as depth maps constructed from stereo or range finder data, rather than just sparse data sets. Finally, Szeliski demonstrates successful applications of the method to several real world problems, including the generation of fractal surfaces, motion estimation without correspondence using sparse range data, and incremental depth from motion. First, he presents a systematic Bayesian probabilistic estimation framework in which we can define and compute the prior model, the sensor model, and the posterior model. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781461289043
Cantidad disponible: 1 disponibles
Imagen de archivo
Bayesian Modeling of Uncertainty in Low-Level Vision (The Springer International Series in Engineering and Computer Science)
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: ABLIING23Mar2716030030118
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Bayesian Modeling of Uncertainty in Low-Level Vision (The Springer International Series in Engineering and Computer Science)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In. Nº de ref. del artículo: ria9781461289043_new
Gastos de envío:
EUR 13,75
De Reino Unido a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Bayesian Modeling of Uncertainty in Low-Level Vision
Publicado por
Springer, Springer Okt 2011, 2011
ISBN 10: 1461289041
ISBN 13: 9781461289043
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski's Ph.D. dissertation at Carnegie Mellon University, presents a Bayesian model for representing and processing uncertainty in low level vision. Recently, probabilistic models have been proposed and used in vision. Sze liski's method has a few distinguishing features that make this monograph im portant and attractive. First, he presents a systematic Bayesian probabilistic estimation framework in which we can define and compute the prior model, the sensor model, and the posterior model. Second, his method represents and computes explicitly not only the best estimates but also the level of uncertainty of those estimates using second order statistics, i.e., the variance and covariance. Third, the algorithms developed are computationally tractable for dense fields, such as depth maps constructed from stereo or range finder data, rather than just sparse data sets. Finally, Szeliski demonstrates successful applications of the method to several real world problems, including the generation of fractal surfaces, motion estimation without correspondence using sparse range data, and incremental depth from motion. 220 pp. Englisch. Nº de ref. del artículo: 9781461289043
Gastos de envío:
EUR 23,00
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 2 disponibles
Imagen del vendedor
Bayesian Modeling of Uncertainty in Low-Level Vision
Impresión bajo demandaLibrería: moluna, Greven, Alemania
Calificación del vendedor: 4 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Rich. Nº de ref. del artículo: 4191454
Gastos de envío:
EUR 48,99
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Bayesian Modeling of Uncertainty in Low-Level Vision
Publicado por
Springer US, Springer US Okt 2011, 2011
ISBN 10: 1461289041
ISBN 13: 9781461289043
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski's Ph.D. dissertation at Carnegie Mellon University, presents a Bayesian model for representing and processing uncertainty in low level vision. Recently, probabilistic models have been proposed and used in vision. Sze liski's method has a few distinguishing features that make this monograph im portant and attractive. First, he presents a systematic Bayesian probabilistic estimation framework in which we can define and compute the prior model, the sensor model, and the posterior model. Second, his method represents and computes explicitly not only the best estimates but also the level of uncertainty of those estimates using second order statistics, i.e., the variance and covariance. Third, the algorithms developed are computationally tractable for dense fields, such as depth maps constructed from stereo or range finder data, rather than just sparse data sets. Finally, Szeliski demonstrates successful applications of the method to several real world problems, including the generation of fractal surfaces, motion estimation without correspondence using sparse range data, and incremental depth from motion.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 220 pp. Englisch. Nº de ref. del artículo: 9781461289043
Gastos de envío:
EUR 60,00
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 1 disponibles
Imagen del vendedor
Bayesian Modeling of Uncertainty in Low-Level Vision
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Vision has to deal with uncertainty. The sensors are noisy, the prior knowledge is uncertain or inaccurate, and the problems of recovering scene information from images are often ill-posed or underconstrained. This research monograph, which is based on Richard Szeliski's Ph.D. dissertation at Carnegie Mellon University, presents a Bayesian model for representing and processing uncertainty in low level vision. Recently, probabilistic models have been proposed and used in vision. Sze liski's method has a few distinguishing features that make this monograph im portant and attractive. First, he presents a systematic Bayesian probabilistic estimation framework in which we can define and compute the prior model, the sensor model, and the posterior model. Second, his method represents and computes explicitly not only the best estimates but also the level of uncertainty of those estimates using second order statistics, i.e., the variance and covariance. Third, the algorithms developed are computationally tractable for dense fields, such as depth maps constructed from stereo or range finder data, rather than just sparse data sets. Finally, Szeliski demonstrates successful applications of the method to several real world problems, including the generation of fractal surfaces, motion estimation without correspondence using sparse range data, and incremental depth from motion. Nº de ref. del artículo: 9781461289043
Gastos de envío:
EUR 61,71
De Alemania a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 1 disponibles
Imagen de archivo
Bayesian Modeling of Uncertainty in Low-Level Vision (The Springer International Series in Engineering and Computer Science, 79)
Librería: Mispah books, Redhill, SURRE, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Paperback. Condición: Like New. Like New. book. Nº de ref. del artículo: ERICA80014612890416
Gastos de envío:
EUR 28,70
De Reino Unido a Estados Unidos de America
Destinos, gastos y plazos de envío
Cantidad disponible: 1 disponibles
Imagen del vendedor
Bayesian Modeling of Uncertainty in Low-level Vision
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 20196201
Cantidad disponible: 15 disponibles