3,161 research outputs found
Geometry Processing of Conventionally Produced Mouse Brain Slice Images
Brain mapping research in most neuroanatomical laboratories relies on
conventional processing techniques, which often introduce histological
artifacts such as tissue tears and tissue loss. In this paper we present
techniques and algorithms for automatic registration and 3D reconstruction of
conventionally produced mouse brain slices in a standardized atlas space. This
is achieved first by constructing a virtual 3D mouse brain model from annotated
slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed
model generates ARA-based slice images corresponding to the microscopic images
of histological brain sections. These image pairs are aligned using a geometric
approach through contour images. Histological artifacts in the microscopic
images are detected and removed using Constrained Delaunay Triangulation before
performing global alignment. Finally, non-linear registration is performed by
solving Laplace's equation with Dirichlet boundary conditions. Our methods
provide significant improvements over previously reported registration
techniques for the tested slices in 3D space, especially on slices with
significant histological artifacts. Further, as an application we count the
number of neurons in various anatomical regions using a dataset of 51
microscopic slices from a single mouse brain. This work represents a
significant contribution to this subfield of neuroscience as it provides tools
to neuroanatomist for analyzing and processing histological data.Comment: 14 pages, 11 figure
Computational models for image contour grouping
Contours are one dimensional curves which may correspond to meaningful entities such as object boundaries. Accurate contour detection will simplify many vision tasks such as object detection and image recognition. Due to the large variety of image content and contour topology, contours are often detected as edge fragments at first, followed by a second step known as {u0300}{u0300}contour grouping'' to connect them. Due to ambiguities in local image patches, contour grouping is essential for constructing globally coherent contour representation. This thesis aims to group contours so that they are consistent with human perception. We draw inspirations from Gestalt principles, which describe perceptual grouping ability of human vision system. In particular, our work is most relevant to the principles of closure, similarity, and past experiences. The first part of our contribution is a new computational model for contour closure. Most of existing contour grouping methods have focused on pixel-wise detection accuracy and ignored the psychological evidences for topological correctness. This chapter proposes a higher-order CRF model to achieve contour closure in the contour domain. We also propose an efficient inference method which is guaranteed to find integer solutions. Tested on the BSDS benchmark, our method achieves a superior contour grouping performance, comparable precision-recall curves, and more visually pleasant results. Our work makes progresses towards a better computational model of human perceptual grouping. The second part is an energy minimization framework for salient contour detection problem. Region cues such as color/texture homogeneity, and contour cues such as local contrast, are both useful for this task. In order to capture both kinds of cues in a joint energy function, topological consistency between both region and contour labels must be satisfied. Our technique makes use of the topological concept of winding numbers. By using a fast method for winding number computation, we find that a small number of linear constraints are sufficient for label consistency. Our method is instantiated by ratio-based energy functions. Due to cue integration, our method obtains improved results. User interaction can also be incorporated to further improve the results. The third part of our contribution is an efficient category-level image contour detector. The objective is to detect contours which most likely belong to a prescribed category. Our method, which is based on three levels of shape representation and non-parametric Bayesian learning, shows flexibility in learning from either human labeled edge images or unlabelled raw images. In both cases, our experiments obtain better contour detection results than competing methods. In addition, our training process is robust even with a considerable size of training samples. In contrast, state-of-the-art methods require more training samples, and often human interventions are required for new category training. Last but not least, in Chapter 7 we also show how to leverage contour information for symmetry detection. Our method is simple yet effective for detecting the symmetric axes of bilaterally symmetric objects in unsegmented natural scene images. Compared with methods based on feature points, our model can often produce better results for the images containing limited texture
Detectability of Cosmic Topology in Generalized Chaplygin Gas Models
If the spatial section of the universe is multiply connected, repeated images
or patterns are expected to be detected observationally. However, due to the
finite distance to the last scattering surface, such pattern repetitions could
be unobservable. This raises the question of whether a given cosmic topology is
detectable, depending on the values of the parameters of the cosmological
model. We study how detectability is affected by the choice of the model itself
for the matter-energy content of the universe, focusing our attention on the
generalized Chaplygin gas (GCG) model for dark matter and dark energy
unification, and investigate how the detectability of cosmic topology depends
on the GCG parameters. We determine to what extent a number of topologies are
detectable for the current observational bounds on these parameters. It emerges
from our results that the choice of GCG as an alternative to the CDM
matter-energy content model has an impact on the detectability of cosmic
topology.Comment: Submitted to A&
Flow pattern analysis for magnetic resonance velocity imaging
Blood flow in the heart is highly complex. Although blood flow patterns have been investigated by both computational modelling and invasive/non-invasive imaging techniques, their evolution and intrinsic connection with cardiovascular disease has yet to be explored. Magnetic resonance (MR) velocity imaging provides a comprehensive distribution of multi-directional in vivo flow distribution so that detailed quantitative analysis of flow patterns is now possible. However, direct visualisation or quantification of vector fields is of little clinical use, especially for inter-subject or serial comparison of changes in flow patterns due to the progression of the disease or in response to therapeutic measures. In order to achieve a comprehensive and integrated description of flow in health and disease, it is necessary to characterise and model both normal and abnormal flows and their effects. To accommodate the diversity of flow patterns in relation to morphological and functional changes, we have described in this thesis an approach of detecting salient topological features prior to analytical assessment of dynamical indices of the flow patterns. To improve the accuracy of quantitative analysis of the evolution of topological flow features, it is essential to restore the original flow fields so that critical points associated with salient flow features can be more reliably detected. We propose a novel framework for the restoration, abstraction, extraction and tracking of flow features such that their dynamic indices can be accurately tracked and quantified. The restoration method is formulated as a constrained optimisation problem to remove the effects of noise and to improve the consistency of the MR velocity data. A computational scheme is derived from the First Order Lagrangian Method for solving the optimisation problem. After restoration, flow abstraction is applied to partition the entire flow field into clusters, each of which is represented by a local linear expansion of its velocity components. This process not only greatly reduces the amount of data required to encode the velocity distribution but also permits an analytical representation of the flow field from which critical points associated with salient flow features can be accurately extracted. After the critical points are extracted, phase portrait theory can be applied to separate them into attracting/repelling focuses, attracting/repelling nodes, planar vortex, or saddle. In this thesis, we have focused on vortical flow features formed in diastole. To track the movement of the vortices within a cardiac cycle, a tracking algorithm based on relaxation labelling is employed. The constraints and parameters used in the tracking algorithm are designed using the characteristics of the vortices. The proposed framework is validated with both simulated and in vivo data acquired from patients with sequential MR examination following myocardial infarction. The main contribution of the thesis is in the new vector field restoration and flow feature abstraction method proposed. They allow the accurate tracking and quantification of dynamic indices associated with salient features so that inter- and intra-subject comparisons can be more easily made. This provides further insight into the evolution of blood flow patterns and permits the establishment of links between blood flow patterns and localised genesis and progression of cardiovascular disease.Open acces
Estimation of vector fields in unconstrained and inequality constrained variational problems for segmentation and registration
Vector fields arise in many problems of computer vision, particularly in non-rigid registration. In this paper, we develop coupled partial differential equations (PDEs) to estimate vector fields that define the deformation between
objects, and the contour or surface that defines the segmentation of the objects as well.We also explore the utility of inequality constraints applied to variational problems in vision such as estimation of deformation fields in non-rigid registration and tracking. To solve inequality constrained vector
field estimation problems, we apply tools from the Kuhn-Tucker theorem in optimization theory. Our technique differs from recently popular joint segmentation and registration algorithms, particularly in its coupled set of PDEs derived from the same set of energy terms for registration and
segmentation. We present both the theory and results that demonstrate our approach
Static non-reciprocity in mechanical metamaterials
Reciprocity is a fundamental principle governing various physical systems,
which ensures that the transfer function between any two points in space is
identical, regardless of geometrical or material asymmetries. Breaking this
transmission symmetry offers enhanced control over signal transport, isolation
and source protection. So far, devices that break reciprocity have been mostly
considered in dynamic systems, for electromagnetic, acoustic and mechanical
wave propagation associated with spatio-temporal variations. Here we show that
it is possible to strongly break reciprocity in static systems, realizing
mechanical metamaterials that, by combining large nonlinearities with suitable
geometrical asymmetries, and possibly topological features, exhibit vastly
different output displacements under excitation from different sides, as well
as one-way displacement amplification. In addition to extending non-reciprocity
and isolation to statics, our work sheds new light on the understanding of
energy propagation in non-linear materials with asymmetric crystalline
structures and topological properties, opening avenues for energy absorption,
conversion and harvesting, soft robotics, prosthetics and optomechanics.Comment: 19 pages, 3 figures, Supplementary information (11 pages and 5
figures
Inflation and Alternatives with Blue Tensor Spectra
We study the tilt of the primordial gravitational waves spectrum. A hint of
blue tilt is shown from analyzing the BICEP2 and POLARBEAR data. Motivated by
this, we explore the possibilities of blue tensor spectra from the very early
universe cosmology models, including null energy condition violating inflation,
inflation with general initial conditions, and string gas cosmology, etc. For
the simplest G-inflation, blue tensor spectrum also implies blue scalar
spectrum. In general, the inflation models with blue tensor spectra indicate
large non-Gaussianities. On the other hand, string gas cosmology predicts blue
tensor spectrum with highly Gaussian fluctuations. If further experiments do
confirm the blue tensor spectrum, non-Gaussianity becomes a distinguishing test
between inflation and alternatives.Comment: 13 pages, 10 figures. v2: references and minor improvements added.
v3: version to appear on JCA
Bound vortex states and exotic lattices in multi-component Bose-Einstein condensates: The role of vortex-vortex interaction
We numerically study the vortex-vortex interaction in multi-component
homogeneous Bose-Einstein condensates within the realm of the Gross-Pitaevskii
theory. We provide strong evidences that pairwise vortex interaction captures
the underlying mechanisms which determine the geometric configuration of the
vortices, such as different lattices in many-vortex states, as well as the
bound vortex states with two (dimer) or three (trimer) vortices. Specifically,
we discuss and apply our theoretical approach to investigate intra- and
inter-component vortex-vortex interactions in two- and three-component
Bose-Einstein condensates, thereby shedding light on the formation of the
exotic vortex configurations. These results correlate with current experimental
efforts in multi-component Bose-Einstein condensates, and the understanding of
the role of vortex interactions in multiband superconductors.Comment: Published in PR
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