78,391 research outputs found
Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning
Cardiovascular disease accounts for 1 in every 4 deaths in United States.
Accurate estimation of structural and functional cardiac parameters is crucial
for both diagnosis and disease management. In this work, we develop an ensemble
learning framework for more accurate and robust left ventricle (LV)
quantification. The framework combines two 1st-level modules: direct estimation
module and a segmentation module. The direct estimation module utilizes
Convolutional Neural Network (CNN) to achieve end-to-end quantification. The
CNN is trained by taking 2D cardiac images as input and cardiac parameters as
output. The segmentation module utilizes a U-Net architecture for obtaining
pixel-wise prediction of the epicardium and endocardium of LV from the
background. The binary U-Net output is then analyzed by a separate CNN for
estimating the cardiac parameters. We then employ linear regression between the
1st-level predictor and ground truth to learn a 2nd-level predictor that
ensembles the results from 1st-level modules for the final estimation.
Preliminary results by testing the proposed framework on the LVQuan18 dataset
show superior performance of the ensemble learning model over the two base
modules.Comment: Jiasha Liu, Xiang Li and Hui Ren contribute equally to this wor
BodyNet: Volumetric Inference of 3D Human Body Shapes
Human shape estimation is an important task for video editing, animation and
fashion industry. Predicting 3D human body shape from natural images, however,
is highly challenging due to factors such as variation in human bodies,
clothing and viewpoint. Prior methods addressing this problem typically attempt
to fit parametric body models with certain priors on pose and shape. In this
work we argue for an alternative representation and propose BodyNet, a neural
network for direct inference of volumetric body shape from a single image.
BodyNet is an end-to-end trainable network that benefits from (i) a volumetric
3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate
supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them
results in performance improvement as demonstrated by our experiments. To
evaluate the method, we fit the SMPL model to our network output and show
state-of-the-art results on the SURREAL and Unite the People datasets,
outperforming recent approaches. Besides achieving state-of-the-art
performance, our method also enables volumetric body-part segmentation.Comment: Appears in: European Conference on Computer Vision 2018 (ECCV 2018).
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Accurate Optical Flow via Direct Cost Volume Processing
We present an optical flow estimation approach that operates on the full
four-dimensional cost volume. This direct approach shares the structural
benefits of leading stereo matching pipelines, which are known to yield high
accuracy. To this day, such approaches have been considered impractical due to
the size of the cost volume. We show that the full four-dimensional cost volume
can be constructed in a fraction of a second due to its regularity. We then
exploit this regularity further by adapting semi-global matching to the
four-dimensional setting. This yields a pipeline that achieves significantly
higher accuracy than state-of-the-art optical flow methods while being faster
than most. Our approach outperforms all published general-purpose optical flow
methods on both Sintel and KITTI 2015 benchmarks.Comment: Published at the Conference on Computer Vision and Pattern
Recognition (CVPR 2017
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