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020 _a9783319270043
040 _aES-MaUEC
050 4 _aQ325.5
_bH863 2016
082 0 4 _a621.382
245 1 0 _aHuman Activity Recognition and Prediction
_cedited by Yun Fu
250 _a1st ed.
260 _aCham
_bSpringer International Publishing
_c2016
300 _a1 recurso en línea (VII, 174 páginas)
_b70 ilustraciones, 6 ilustraciones en color
336 _aTexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
505 0 _aIntroduction -- Action and Activities -- Action Recognition and Human Interaction -- Multimodal Action Recognition -- RGB-D Action Recognition -- Actionlets and Activity Prediction -- Time Series Modeling for Activity Prediction -- RGB-D Action Prediction.
520 3 _aThis book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. .
710 2 _aSpringerLink (Online service)
_0Local
_9106996
942 _2lcc
_cLE
988 _aEBOOK, asignarmaterias , EBSPRINGER
650 0 4 _9669495
_aProceso de imágenes
_0
650 0 7 _aBiometría
_0
_2embne
_9139105
700 1 _aFu, Yun
_eeditor literario
_0Local
_998109
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-27004-3
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901 _ai9783319270043
907 _a.b12945493
_b10-10-17
_c21-11-16
998 _am
_a_alco
_a_vill
_b - -
_cm
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945 _aQ325.5 H863 2016 EB
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_z06-04-17
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