67,979 research outputs found
Staple: Complementary Learners for Real-Time Tracking
Correlation Filter-based trackers have recently achieved excellent
performance, showing great robustness to challenging situations exhibiting
motion blur and illumination changes. However, since the model that they learn
depends strongly on the spatial layout of the tracked object, they are
notoriously sensitive to deformation. Models based on colour statistics have
complementary traits: they cope well with variation in shape, but suffer when
illumination is not consistent throughout a sequence. Moreover, colour
distributions alone can be insufficiently discriminative. In this paper, we
show that a simple tracker combining complementary cues in a ridge regression
framework can operate faster than 80 FPS and outperform not only all entries in
the popular VOT14 competition, but also recent and far more sophisticated
trackers according to multiple benchmarks.Comment: To appear in CVPR 201
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
In this work, we adapt a method based on multiple hypothesis tracking (MHT)
that has been shown to give state-of-the-art vessel segmentation results in
interactive settings, for the purpose of extracting trees. Regularly spaced
tubular templates are fit to image data forming local hypotheses. These local
hypotheses are used to construct the MHT tree, which is then traversed to make
segmentation decisions. However, some critical parameters in this method are
scale-dependent and have an adverse effect when tracking structures of varying
dimensions. We propose to use statistical ranking of local hypotheses in
constructing the MHT tree, which yields a probabilistic interpretation of
scores across scales and helps alleviate the scale-dependence of MHT
parameters. This enables our method to track trees starting from a single seed
point. Our method is evaluated on chest CT data to extract airway trees and
coronary arteries. In both cases, we show that our method performs
significantly better than the original MHT method.Comment: Accepted for publication at the International Journal of Medical
Physics and Practic
Finite Element Based Tracking of Deforming Surfaces
We present an approach to robustly track the geometry of an object that
deforms over time from a set of input point clouds captured from a single
viewpoint. The deformations we consider are caused by applying forces to known
locations on the object's surface. Our method combines the use of prior
information on the geometry of the object modeled by a smooth template and the
use of a linear finite element method to predict the deformation. This allows
the accurate reconstruction of both the observed and the unobserved sides of
the object. We present tracking results for noisy low-quality point clouds
acquired by either a stereo camera or a depth camera, and simulations with
point clouds corrupted by different error terms. We show that our method is
also applicable to large non-linear deformations.Comment: additional experiment
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