6,281 research outputs found
Automatic landmark annotation and dense correspondence registration for 3D human facial images
Dense surface registration of three-dimensional (3D) human facial images
holds great potential for studies of human trait diversity, disease genetics,
and forensics. Non-rigid registration is particularly useful for establishing
dense anatomical correspondences between faces. Here we describe a novel
non-rigid registration method for fully automatic 3D facial image mapping. This
method comprises two steps: first, seventeen facial landmarks are automatically
annotated, mainly via PCA-based feature recognition following 3D-to-2D data
transformation. Second, an efficient thin-plate spline (TPS) protocol is used
to establish the dense anatomical correspondence between facial images, under
the guidance of the predefined landmarks. We demonstrate that this method is
robust and highly accurate, even for different ethnicities. The average face is
calculated for individuals of Han Chinese and Uyghur origins. While fully
automatic and computationally efficient, this method enables high-throughput
analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl
A test for model specification of diffusion processes
We propose a test for model specification of a parametric diffusion process
based on a kernel estimation of the transitional density of the process. The
empirical likelihood is used to formulate a statistic, for each kernel
smoothing bandwidth, which is effectively a Studentized -distance between
the kernel transitional density estimator and the parametric transitional
density implied by the parametric process. To reduce the sensitivity of the
test on smoothing bandwidth choice, the final test statistic is constructed by
combining the empirical likelihood statistics over a set of smoothing
bandwidths. To better capture the finite sample distribution of the test
statistic and data dependence, the critical value of the test is obtained by a
parametric bootstrap procedure. Properties of the test are evaluated
asymptotically and numerically by simulation and by a real data example.Comment: Published in at http://dx.doi.org/10.1214/009053607000000659 the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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