28,913 research outputs found
Comment: Struggles with Survey Weighting and Regression Modeling
Comment: Struggles with Survey Weighting and Regression Modeling
[arXiv:0710.5005]Comment: Published in at http://dx.doi.org/10.1214/088342307000000186 the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Observations on recruitment of postlarval spiny lobsters, Panulirus argus, to the south Florida coast
(Document has 34 pages.
Reduction of electrical noise and crosstalk in guidance system components Progress report, Sep. 1965
Servoamplifier and amplifier-detector inspection for gyroscope and accelerometer servo loop
Real-Time Human Motion Capture with Multiple Depth Cameras
Commonly used human motion capture systems require intrusive attachment of
markers that are visually tracked with multiple cameras. In this work we
present an efficient and inexpensive solution to markerless motion capture
using only a few Kinect sensors. Unlike the previous work on 3d pose estimation
using a single depth camera, we relax constraints on the camera location and do
not assume a co-operative user. We apply recent image segmentation techniques
to depth images and use curriculum learning to train our system on purely
synthetic data. Our method accurately localizes body parts without requiring an
explicit shape model. The body joint locations are then recovered by combining
evidence from multiple views in real-time. We also introduce a dataset of ~6
million synthetic depth frames for pose estimation from multiple cameras and
exceed state-of-the-art results on the Berkeley MHAD dataset.Comment: Accepted to computer robot vision 201
Block-Conditional Missing at Random Models for Missing Data
Two major ideas in the analysis of missing data are (a) the EM algorithm
[Dempster, Laird and Rubin, J. Roy. Statist. Soc. Ser. B 39 (1977) 1--38] for
maximum likelihood (ML) estimation, and (b) the formulation of models for the
joint distribution of the data and missing data indicators , and
associated "missing at random"; (MAR) condition under which a model for
is unnecessary [Rubin, Biometrika 63 (1976) 581--592]. Most previous work has
treated and as single blocks, yielding selection or pattern-mixture
models depending on how their joint distribution is factorized. This paper
explores "block-sequential"; models that interleave subsets of the variables
and their missing data indicators, and then make parameter restrictions based
on assumptions in each block. These include models that are not MAR. We examine
a subclass of block-sequential models we call block-conditional MAR (BCMAR)
models, and an associated block-monotone reduced likelihood strategy that
typically yields consistent estimates by selectively discarding some data.
Alternatively, full ML estimation can often be achieved via the EM algorithm.
We examine in some detail BCMAR models for the case of two multinomially
distributed categorical variables, and a two block structure where the first
block is categorical and the second block arises from a (possibly multivariate)
exponential family distribution.Comment: Published in at http://dx.doi.org/10.1214/10-STS344 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
The measurement of applied forces during anterior single rod correction of adolescent idiopathic scoliosis
Adolescent idiopathic scoliosis (AIS) is the most common form of spinal deformity in paediatrics, prevalent in approximately 2-4% of the general population. While it is a complex three-dimensional deformity, it is clinically characterised by an abnormal lateral curvature of the spine. The treatment for severe deformity is surgical correction with the use of structural implants. Anterior single rod correction employs a solid rod connected to the anterior spine via vertebral body screws. Correction is achieved by applying compression between adjacent vertebral body screws, before locking each screw onto the rod. Biomechanical complication rates have been reported as high as 20.8%, and include rod breakage, screw pull-out and loss of correction. Currently, the corrective forces applied to the spine are unknown. These forces are important variables to consider in understanding the biomechanics of scoliosis correction. The purpose of this study was to measure these forces intra-operatively during anterior single rod AIS correction
Play and Learn: Using Video Games to Train Computer Vision Models
Video games are a compelling source of annotated data as they can readily
provide fine-grained groundtruth for diverse tasks. However, it is not clear
whether the synthetically generated data has enough resemblance to the
real-world images to improve the performance of computer vision models in
practice. We present experiments assessing the effectiveness on real-world data
of systems trained on synthetic RGB images that are extracted from a video
game. We collected over 60000 synthetic samples from a modern video game with
similar conditions to the real-world CamVid and Cityscapes datasets. We provide
several experiments to demonstrate that the synthetically generated RGB images
can be used to improve the performance of deep neural networks on both image
segmentation and depth estimation. These results show that a convolutional
network trained on synthetic data achieves a similar test error to a network
that is trained on real-world data for dense image classification. Furthermore,
the synthetically generated RGB images can provide similar or better results
compared to the real-world datasets if a simple domain adaptation technique is
applied. Our results suggest that collaboration with game developers for an
accessible interface to gather data is potentially a fruitful direction for
future work in computer vision.Comment: To appear in the British Machine Vision Conference (BMVC), September
2016. -v2: fixed a typo in the reference
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