Title: Estimating 3D Hand Pose from a Cluttered Image
Authors: Vassilis Athitsos and Stan Scalaroff
(CVPR 2003)
Abstract:
A method is proposed that can generate a ranked list of
plausible three-dimensional hand configurations that best
match an input image. Hand pose estimation is formulated
as an image database indexing problem, where the closest
matches for an input hand image are retrieved from a large
database of synthetic hand images. In contrast to previous
approaches, the system can function in the presence of
clutter, thanks to two novel clutter-tolerant indexing methods.
First, a computationally efficient approximation of
the image-to-model chamfer distance is obtained by embedding
binary edge images into a high-dimensional Euclidean
space. Second, a general-purpose, probabilistic line matching
method identifies those line segment correspondences
between model and input images that are the least likely to
have occurred by chance. The performance of this cluttertolerant
approach is demonstrated in quantitative experiments
with hundreds of real hand images.
Paper download: [Link]
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