44,115 research outputs found
Scan registration for autonomous mining vehicles using 3D-NDT
Scan registration is an essential subtask when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalization and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Strasser, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory efficient scan surface representation
Gaussian Process Morphable Models
Statistical shape models (SSMs) represent a class of shapes as a normal
distribution of point variations, whose parameters are estimated from example
shapes. Principal component analysis (PCA) is applied to obtain a
low-dimensional representation of the shape variation in terms of the leading
principal components. In this paper, we propose a generalization of SSMs,
called Gaussian Process Morphable Models (GPMMs). We model the shape variations
with a Gaussian process, which we represent using the leading components of its
Karhunen-Loeve expansion. To compute the expansion, we make use of an
approximation scheme based on the Nystrom method. The resulting model can be
seen as a continuous analogon of an SSM. However, while for SSMs the shape
variation is restricted to the span of the example data, with GPMMs we can
define the shape variation using any Gaussian process. For example, we can
build shape models that correspond to classical spline models, and thus do not
require any example data. Furthermore, Gaussian processes make it possible to
combine different models. For example, an SSM can be extended with a spline
model, to obtain a model that incorporates learned shape characteristics, but
is flexible enough to explain shapes that cannot be represented by the SSM. We
introduce a simple algorithm for fitting a GPMM to a surface or image. This
results in a non-rigid registration approach, whose regularization properties
are defined by a GPMM. We show how we can obtain different registration
schemes,including methods for multi-scale, spatially-varying or hybrid
registration, by constructing an appropriate GPMM. As our approach strictly
separates modelling from the fitting process, this is all achieved without
changes to the fitting algorithm. We show the applicability and versatility of
GPMMs on a clinical use case, where the goal is the model-based segmentation of
3D forearm images
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images
We present a novel kernel regression framework for smoothing scalar surface
data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel
constructed from the eigenfunctions, we formulate a new bivariate kernel
regression framework as a weighted eigenfunction expansion with the heat kernel
as the weights. The new kernel regression is mathematically equivalent to
isotropic heat diffusion, kernel smoothing and recently popular diffusion
wavelets. Unlike many previous partial differential equation based approaches
involving diffusion, our approach represents the solution of diffusion
analytically, reducing numerical inaccuracy and slow convergence. The numerical
implementation is validated on a unit sphere using spherical harmonics. As an
illustration, we have applied the method in characterizing the localized growth
pattern of mandible surfaces obtained in CT images from subjects between ages 0
and 20 years by regressing the length of displacement vectors with respect to
the template surface.Comment: Accepted in Medical Image Analysi
Symmetry-guided nonrigid registration: the case for distortion correction in multidimensional photoemission spectroscopy
Image symmetrization is an effective strategy to correct symmetry distortion
in experimental data for which symmetry is essential in the subsequent
analysis. In the process, a coordinate transform, the symmetrization transform,
is required to undo the distortion. The transform may be determined by image
registration (i.e. alignment) with symmetry constraints imposed in the
registration target and in the iterative parameter tuning, which we call
symmetry-guided registration. An example use case of image symmetrization is
found in electronic band structure mapping by multidimensional photoemission
spectroscopy, which employs a 3D time-of-flight detector to measure electrons
sorted into the momentum (, ) and energy () coordinates. In
reality, imperfect instrument design, sample geometry and experimental settings
cause distortion of the photoelectron trajectories and, therefore, the symmetry
in the measured band structure, which hinders the full understanding and use of
the volumetric datasets. We demonstrate that symmetry-guided registration can
correct the symmetry distortion in the momentum-resolved photoemission
patterns. Using proposed symmetry metrics, we show quantitatively that the
iterative approach to symmetrization outperforms its non-iterative counterpart
in the restored symmetry of the outcome while preserving the average shape of
the photoemission pattern. Our approach is generalizable to distortion
corrections in different types of symmetries and should also find applications
in other experimental methods that produce images with similar features
High Angular Resolution Mid-infrared Imaging of Young Stars in Orion BN/KL
We present Keck LWS images of the Orion BN/KL star forming region obtained in
the first multi-wavelength study to have 0.3-0.5" resolution from 4.7 to 22
microns. The young stellar objects designated infrared source-n and radio
source-I are believed to dominate the BN/KL region. We have detected extended
emission from a probable accretion disk around source-n but infer a stellar
luminosity on the order of only 2000 Lsun. Although source-I is believed to be
more luminous, we do not detect an infrared counterpart even at the longest
wavelengths. However, we resolve the closeby infrared source, IRc2, into an arc
of knots ~1000 AU long at all wavelengths. Although the physical relation of
source-I to IRc2 remains ambiguous, we suggest these sources mark a high
density core (10^7-10^8 pc^-3 over 1000 AU) within the larger BN/KL star
forming cluster. The high density may be a consequence of the core being young
and heavily embedded. We suggest the energetics of the BN/KL region may be
dominated by this cluster core rather than one or two individual sources.Comment: 13 pages including 3 color figures. Accepted to The Astrophysical
Journal Letters pending slight reduction in length. High resolution figures
(jpeg) may be found at
http://cfa-www.harvard.edu/~lincoln/keck.bnkl.midir.ppr
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