16,444 research outputs found
Asymptotic goodness-of-fit tests for the Palm mark distribution of stationary point processes with correlated marks
We consider spatially homogeneous marked point patterns in an unboundedly
expanding convex sampling window. Our main objective is to identify the
distribution of the typical mark by constructing an asymptotic
-goodness-of-fit test. The corresponding test statistic is based on a
natural empirical version of the Palm mark distribution and a smoothed
covariance estimator which turns out to be mean square consistent. Our approach
does not require independent marks and allows dependences between the mark
field and the point pattern. Instead we impose a suitable -mixing
condition on the underlying stationary marked point process which can be
checked for a number of Poisson-based models and, in particular, in the case of
geostatistical marking. In order to study test performance, our test approach
is applied to detect anisotropy of specific Boolean models.Comment: Published in at http://dx.doi.org/10.3150/13-BEJ523 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm). arXiv admin
note: substantial text overlap with arXiv:1205.504
Hybrid One-Shot 3D Hand Pose Estimation by Exploiting Uncertainties
Model-based approaches to 3D hand tracking have been shown to perform well in
a wide range of scenarios. However, they require initialisation and cannot
recover easily from tracking failures that occur due to fast hand motions.
Data-driven approaches, on the other hand, can quickly deliver a solution, but
the results often suffer from lower accuracy or missing anatomical validity
compared to those obtained from model-based approaches. In this work we propose
a hybrid approach for hand pose estimation from a single depth image. First, a
learned regressor is employed to deliver multiple initial hypotheses for the 3D
position of each hand joint. Subsequently, the kinematic parameters of a 3D
hand model are found by deliberately exploiting the inherent uncertainty of the
inferred joint proposals. This way, the method provides anatomically valid and
accurate solutions without requiring manual initialisation or suffering from
track losses. Quantitative results on several standard datasets demonstrate
that the proposed method outperforms state-of-the-art representatives of the
model-based, data-driven and hybrid paradigms.Comment: BMVC 2015 (oral); see also
http://lrs.icg.tugraz.at/research/hybridhape
Automatic recognition of fingerspelled words in British Sign Language
We investigate the problem of recognizing words from
video, fingerspelled using the British Sign Language (BSL)
fingerspelling alphabet. This is a challenging task since the
BSL alphabet involves both hands occluding each other, and
contains signs which are ambiguous from the observer’s
viewpoint. The main contributions of our work include:
(i) recognition based on hand shape alone, not requiring
motion cues; (ii) robust visual features for hand shape
recognition; (iii) scalability to large lexicon recognition
with no re-training.
We report results on a dataset of 1,000 low quality webcam
videos of 100 words. The proposed method achieves a
word recognition accuracy of 98.9%
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