Multi-view Appearance-based 3D Hand Pose Estimation

Authors:
Haiying Guan;Jae Sik Chang;Longbin Chen;Rogerio S. Feris;Matthew Turk
Affiliations:
University of California, Santa Barbara, CA, USA;University of California, Santa Barbara, CA, USA;University of California, Santa Barbara, CA, USA;University of California, Santa Barbara, CA, USA;University of California, Santa Barbara, CA, USA
Venue:
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Year:
2006

Citing 0
Cited 1

Using hierarchical temporal memory for vision-based hand shape recognition under large variations in hand's rotation

ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a novel approach to appearance-based hand pose estimation which relies on multiple cameras to improve accuracy and resolve ambiguities caused by selfocclusions. Rather than estimating 3D geometry as most previous multi-view imaging systems, our approach uses multiple views to extend current exemplar-based methods that estimate hand pose by matching a probe image with a large discrete set of labeled hand pose images. We formulate the problem in a MAP (maximum a posteriori) framework, where the information from multiple cameras is fused to provide reliable hand pose estimation. Our quantitative experimental results show that correct estimation rate is much higher using our multi-view approach than using a single-view approach.