9,505 research outputs found
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
See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG
The Histogram of Oriented Gradient (HOG) descriptor has led to many advances
in computer vision over the last decade and is still part of many state of the
art approaches. We realize that the associated feature computation is piecewise
differentiable and therefore many pipelines which build on HOG can be made
differentiable. This lends to advanced introspection as well as opportunities
for end-to-end optimization. We present our implementation of HOG based
on the auto-differentiation toolbox Chumpy and show applications to pre-image
visualization and pose estimation which extends the existing differentiable
renderer OpenDR pipeline. Both applications improve on the respective
state-of-the-art HOG approaches
Temporally Coherent General Dynamic Scene Reconstruction
Existing techniques for dynamic scene reconstruction from multiple
wide-baseline cameras primarily focus on reconstruction in controlled
environments, with fixed calibrated cameras and strong prior constraints. This
paper introduces a general approach to obtain a 4D representation of complex
dynamic scenes from multi-view wide-baseline static or moving cameras without
prior knowledge of the scene structure, appearance, or illumination.
Contributions of the work are: An automatic method for initial coarse
reconstruction to initialize joint estimation; Sparse-to-dense temporal
correspondence integrated with joint multi-view segmentation and reconstruction
to introduce temporal coherence; and a general robust approach for joint
segmentation refinement and dense reconstruction of dynamic scenes by
introducing shape constraint. Comparison with state-of-the-art approaches on a
variety of complex indoor and outdoor scenes, demonstrates improved accuracy in
both multi-view segmentation and dense reconstruction. This paper demonstrates
unsupervised reconstruction of complete temporally coherent 4D scene models
with improved non-rigid object segmentation and shape reconstruction and its
application to free-viewpoint rendering and virtual reality.Comment: Submitted to IJCV 2019. arXiv admin note: substantial text overlap
with arXiv:1603.0338
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, such a structure has to be learned from the data in a bottom-up
fashion. In recent times, volumetric approaches in which the motion is captured
from a number of cameras and a voxel-set representation of the body is built
from the camera views, have gained ground due to attractive features such as
inherent view-invariance and robustness to occlusions. Automatic, unsupervised
segmentation of moving bodies along entire sequences, in a temporally-coherent
and robust way, has the potential to provide a means of constructing a
bottom-up model of the moving body, and track motion cues that may be later
exploited for motion classification. Spectral methods such as locally linear
embedding (LLE) can be useful in this context, as they preserve "protrusions",
i.e., high-curvature regions of the 3D volume, of articulated shapes, while
improving their separation in a lower dimensional space, making them in this
way easier to cluster. In this paper we therefore propose a spectral approach
to unsupervised and temporally-coherent body-protrusion segmentation along time
sequences. Volumetric shapes are clustered in an embedding space, clusters are
propagated in time to ensure coherence, and merged or split to accommodate
changes in the body's topology. Experiments on both synthetic and real
sequences of dense voxel-set data are shown. This supports the ability of the
proposed method to cluster body-parts consistently over time in a totally
unsupervised fashion, its robustness to sampling density and shape quality, and
its potential for bottom-up model constructionComment: 31 pages, 26 figure
- …