NDLI: Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference

Content Provider	IEEE Xplore Digital Library
Author	Sminchisescu, C. Kanaujia, A. Metaxas, D.
Copyright Year	2006
Description	Author affiliation: TTI-C (Sminchisescu, C.)
Abstract	We present an algorithm for jointly learning a consistent bidirectional generative-recognition model that combines top-down and bottom-up processing for monocular 3d human motion reconstruction. Learning progresses in alternative stages of self-training that optimize the probability of the image evidence: the recognition model is tunned using samples from the generative model and the generative model is optimized to produce inferences close to the ones predicted by the current recognition model. At equilibrium, the two models are consistent. During on-line inference, we scan the image at multiple locations and predict 3d human poses using the recognition model. But this implicitly includes one-shot generative consistency feedback. The framework provides a uniform treatment of human detection, 3d initialization and 3d recovery from transient failure. Our experimental results show that this procedure is promising for the automatic reconstruction of human motion in more natural scene settings with background clutter and occlusion.
Starting Page	1743
Ending Page	1752
File Size	981654
Page Count	10
File Format	PDF
ISBN	0769525970
ISSN	10636919
DOI	10.1109/CVPR.2006.169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Predictive models Image recognition Biological system modeling Image reconstruction Feedback AC generators Layout Image analysis Detectors
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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