4,769 research outputs found
Hierarchical bounding structures for efficient virial computations: Towards a realistic molecular description of cholesterics
We detail the application of bounding volume hierarchies to accelerate
second-virial evaluations for arbitrary complex particles interacting through
hard and soft finite-range potentials. This procedure, based on the
construction of neighbour lists through the combined use of recursive
atom-decomposition techniques and binary overlap search schemes, is shown to
scale sub-logarithmically with particle resolution in the case of molecular
systems with high aspect ratios. Its implementation within an efficient
numerical and theoretical framework based on classical density functional
theory enables us to investigate the cholesteric self-assembly of a wide range
of experimentally-relevant particle models. We illustrate the method through
the determination of the cholesteric behaviour of hard, structurally-resolved
twisted cuboids, and report quantitative evidence of the long-predicted phase
handedness inversion with increasing particle thread angles near the
phenomenological threshold value of . Our results further highlight
the complex relationship between microscopic structure and helical twisting
power in such model systems, which may be attributed to subtle geometric
variations of their chiral excluded-volume manifold
A novel model-based 3D+time left ventricular segmentation technique
A common approach to model-based segmentation is to assume a top-down modelling strategy. However, this is not feasible for complex 3D+time structures such as the cardiac left ventricle due to increased training requirements, aligning difficulties and local minima in resulting models. As our main contribution, we present an alternate bottom-up modelling approach. By combining the variation captured in multiple dimensionally-targeted models at segmentation-time we create a scalable segmentation framework that does not suffer from the ’curse of dimensionality’. Our second contribution involves a flexible contour coupling technique that allows our segmentation method to adapt to unseen contour configurations outside the training set. This is used to identify the endo- and epi-cardium contours of the left ventricle by coupling them at segmentationtime, instead of at model-time. We apply our approach to 33 3D+time MRI cardiac datasets and perform comprehensive evaluation against several state-of-the-art works. Quantitative evaluation illustrates that our method requires significantly less training than state-of-the-art model-based methods, while maintaining or improving segmentation accuracy
Curve-Based Shape Matching Methods and Applications
One of the main cues we use in our everyday life when interacting with the environment is shape.
For example, we use shape information to recognise a chair, grasp a cup, perceive traffic signs and
solve jigsaw puzzles. We also use shape when dealing with more sophisticated tasks, such as the
medical diagnosis of radiographs or the restoration of archaeological artifacts. While the perception
of shape and its use is a natural ability of human beings, endowing machines with such skills is
not straightforward. However, the exploitation of shape cues is important for the development of
competent computer methods that will automatically perform tasks such as those just mentioned.
With this aim, the present work proposes computer methods which use shape to tackle two important
tasks, namely packing and object recognition.
The packing problem arises in a variety of applications in industry, where the placement of a set
of two-dimensional shapes on a surface such that no shapes overlap and the uncovered surface area
is minimised is important. Given that this problem is NP-complete, we propose a heuristic method
which searches for a solution of good quality, though not necessarily the optimal one, within a reasonable
computation time. The proposed method adopts a pictorial representation and employs a greedy
algorithm which uses a shape matching module in order to dynamically select the order and the pose
of the parts to be placed based on the “gaps” appearing in the layout during the execution.
This thesis further investigates shape matching in the context of object recognition and first considers
the case where the target object and the input scene are represented by their silhouettes. Two distinct
methods are proposed; the first method follows a local string matching approach, while the second
one adopts a global optimisation approach using dynamic programming. Their use of silhouettes,
however, rules out the consideration of any internal contours that might appear in the input scene,
and in order to address this limitation, we later propose a graph-based scheme that performs shape
matching incorporating information from both internal and external contours. Finally, we lift the assumption
made that input data are available in the form of closed curves, and present a method which
can robustly perform object recognition using curve fragments (edges) as input evidence. Experiments
conducted with synthetic and real images, involving rigid and deformable objects, show the
robustness of the proposed methods with respect to geometrical transformations, heavy clutter and
substantial occlusion
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