Multimodal Deep Autoencoder for Human Pose Recovery

Video-based human pose recovery is usually conducted by retrieving relevant poses using image features. In the retrieving process, the mapping between 2D images and 3D poses is assumed to be linear in most of the traditional methods. However, their relationships are inherently non-linear, which limi...

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Veröffentlicht in:	IEEE transactions on image processing Jg. 24; H. 12; S. 5659 - 5670
Hauptverfasser:	Hong, Chaoqun, Yu, Jun, Wan, Jian, Tao, Dacheng, Wang, Meng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2015 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial Intelligence Back propagation deep learning Electronic mail Feature extraction Female Graphs Hidden Markov models Human Human pose recovery Humans hypergraph Image Processing, Computer-Assisted - methods Machine learning Male Mapping multi-modal learning Neural networks Nonlinearity Posture - physiology Recovery Three dimensional Three-dimensional displays Two dimensional Visualization deep learning hypergraph Human pose recovery multi-modal learning back propagation
ISSN:	1057-7149, 1941-0042, 1941-0042
Online-Zugang:	Volltext
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