LEADER 00000nam a22005415i 4500 
001    978-3-319-04561-0 
003    DE-He213 
005    20151029221315.0 
007    cr nn 008mamaa 
008    140125s2014    gw |    s    |||| 0|eng d 
020    9783319045610|9978-3-319-04561-0 
024 7  10.1007/978-3-319-04561-0|2doi 
040    |dES-ZaU 
072  7 UYT|2bicssc 
072  7 UYQV|2bicssc 
072  7 COM012000|2bisacsh 
072  7 COM016000|2bisacsh 
082 04 006.6|223 
082 04 006.37|223 
100 1  Wang, Jiang.|eauthor. 
245 10 Human Action Recognition with Depth Cameras|h[electronic 
       resource] /|cby Jiang Wang, Zicheng Liu, Ying Wu. 
264  1 Cham :|bSpringer International Publishing :|bImprint: 
300    VIII, 59 p. 32 illus., 9 illus. in color.|bonline 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file|bPDF|2rda 
490 1  SpringerBriefs in Computer Science,|x2191-5768 
490 0  Springer eBooks.|aComputer Science 
505 0  Introduction -- Learning Actionlet Ensemble for 3D Human 
       Action Recognition -- Random Occupancy Patterns -- 
520    Action recognition is an enabling technology for many real
       world applications, such as human-computer interaction, 
       surveillance, video retrieval, retirement home monitoring,
       and robotics. In the past decade, it has attracted a great
       amount of interest in the research community. Recently, 
       the commoditization of depth sensors has generated much 
       excitement in action recognition from depth sensors. New 
       depth sensor technology has enabled many applications that
       were not feasible before. On one hand, action recognition 
       becomes far easier with depth sensors. On the other hand, 
       the drive to recognize more complex actions presents new 
       challenges. One crucial aspect of action recognition is to
       extract discriminative features. The depth maps have 
       completely different characteristics from the RGB images. 
       Directly applying features designed for RGB images does 
       not work. Complex actions usually involve complicated 
       temporal structures, human-object interactions, and person
       -person contacts. New machine learning algorithms need to 
       be developed to learn these complex structures. This work 
       enables the reader to quickly familiarize themselves with 
       the latest research in depth-sensor based action 
       recognition, and to gain a deeper understanding of 
       recently developed techniques. It will be of great use for
       both researchers and practitioners who are interested in 
       human action recognition with depth sensors. The text 
       focuses on feature representation and machine learning 
       algorithms for action recognition from depth sensors. 
       After presenting a comprehensive overview of the state of 
       the art in action recognition from depth data, the authors
       then provide in-depth descriptions of their recently 
       developed feature representations and machine learning 
       techniques, including lower-level depth and skeleton 
       features, higher-level representations to model the 
       temporal structure and human-object interactions, and 
       feature selection techniques for occlusion handling. 
650  0 Computer science. 
650  0 User interfaces (Computer systems). 
650  0 Image processing. 
650  0 Biometrics (Biology). 
650 14 Computer Science. 
650 24 Image Processing and Computer Vision. 
650 24 Biometrics. 
650 24 User Interfaces and Human Computer Interaction. 
700 1  Liu, Zicheng.,|eauthor. 
700 1  Wu, Ying.,|eauthor. 
710 2  SpringerLink (Online service) 
773 0  |tSpringer eBooks 
776 08 |iPrinted edition:|z9783319045603 
830  0 SpringerBriefs in Computer Science,|x2191-5768 
856 40 |uhttps://cuarzo.unizar.es:9443/login?url=https://
       dx.doi.org/10.1007/978-3-319-04561-0|zAcceso al texto