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008			230211s2011 sz | s |||| 0|eng d
020			_a9783031015571
024	7		_a10.1007/978-3-031-01557-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2011 EB
100	1		_aHoiem, Derek _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686726
245	1	0	_aRepresentations and Techniques for 3D Object Recognition and Scene Interpretation _cby Derek Hoiem, Silvio Savarese
250			_a1st edition 2011
264		1	_aCham _bSpringer International Publishing _c2011
300			_a1 recurso en línea (XXI, 147 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aBackground on 3D Scene Models -- Single-view Geometry -- Modeling the Physical Scene -- Categorizing Images and Regions -- Examples of 3D Scene Interpretation -- Background on 3D Recognition -- Modeling 3D Objects -- Recognizing and Understanding 3D Objects -- Examples of 2D 1/2 Layout Models -- Reasoning about Objects and Scenes -- Cascades of Classifiers -- Conclusion and Future Directions.
520			_aOne of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions.
988			_aSynthesis Collection of Technology_2011
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9670117 _aSistemas de imágenes tridimensionales
700	1		_aSavarese, Silvio _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686727
776	0	8	_iPrinted edition: _z9783031004292
776	0	8	_iPrinted edition: _z9783031026850
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01557-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI