65,673 research outputs found
From 3D Point Clouds to Pose-Normalised Depth Maps
We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)
Learning Dynamic Feature Selection for Fast Sequential Prediction
We present paired learning and inference algorithms for significantly
reducing computation and increasing speed of the vector dot products in the
classifiers that are at the heart of many NLP components. This is accomplished
by partitioning the features into a sequence of templates which are ordered
such that high confidence can often be reached using only a small fraction of
all features. Parameter estimation is arranged to maximize accuracy and early
confidence in this sequence. Our approach is simpler and better suited to NLP
than other related cascade methods. We present experiments in left-to-right
part-of-speech tagging, named entity recognition, and transition-based
dependency parsing. On the typical benchmarking datasets we can preserve POS
tagging accuracy above 97% and parsing LAS above 88.5% both with over a
five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase
in speed.Comment: Appears in The 53rd Annual Meeting of the Association for
Computational Linguistics, Beijing, China, July 201
Dual-fisheye lens stitching for 360-degree imaging
Dual-fisheye lens cameras have been increasingly used for 360-degree
immersive imaging. However, the limited overlapping field of views and
misalignment between the two lenses give rise to visible discontinuities in the
stitching boundaries. This paper introduces a novel method for dual-fisheye
camera stitching that adaptively minimizes the discontinuities in the
overlapping regions to generate full spherical 360-degree images. Results show
that this approach can produce good quality stitched images for Samsung Gear
360 -- a dual-fisheye camera, even with hard-to-stitch objects in the stitching
borders.Comment: ICASSP 17 preprint, Proc. of the 42nd IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, March
201
A simple and objective method for reproducible resting state network (RSN) detection in fMRI
Spatial Independent Component Analysis (ICA) decomposes the time by space
functional MRI (fMRI) matrix into a set of 1-D basis time courses and their
associated 3-D spatial maps that are optimized for mutual independence. When
applied to resting state fMRI (rsfMRI), ICA produces several spatial
independent components (ICs) that seem to have biological relevance - the
so-called resting state networks (RSNs). The ICA problem is well posed when the
true data generating process follows a linear mixture of ICs model in terms of
the identifiability of the mixing matrix. However, the contrast function used
for promoting mutual independence in ICA is dependent on the finite amount of
observed data and is potentially non-convex with multiple local minima. Hence,
each run of ICA could produce potentially different IC estimates even for the
same data. One technique to deal with this run-to-run variability of ICA was
proposed by Yang et al. (2008) in their algorithm RAICAR which allows for the
selection of only those ICs that have a high run-to-run reproducibility. We
propose an enhancement to the original RAICAR algorithm that enables us to
assign reproducibility p-values to each IC and allows for an objective
assessment of both within subject and across subjects reproducibility. We call
the resulting algorithm RAICAR-N (N stands for null hypothesis test), and we
have applied it to publicly available human rsfMRI data (http://www.nitrc.org).
Our reproducibility analyses indicated that many of the published RSNs in
rsfMRI literature are highly reproducible. However, we found several other RSNs
that are highly reproducible but not frequently listed in the literature.Comment: 54 pages, 13 figure
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