848 research outputs found
On Open and Strong-Scaling Tools for Atom Probe Crystallography: High-Throughput Methods for Indexing Crystal Structure and Orientation
Volumetric crystal structure indexing and orientation mapping are key data
processing steps for virtually any quantitative study of spatial correlations
between the local chemistry and the microstructure of a material. For electron
and X-ray diffraction methods it is possible to develop indexing tools which
compare measured and analytically computed patterns to decode the structure and
relative orientation within local regions of interest. Consequently, a number
of numerically efficient and automated software tools exist to solve the above
characterisation tasks.
For atom probe tomography (APT) experiments, however, the strategy of making
comparisons between measured and analytically computed patterns is less robust
because many APT datasets may contain substantial noise. Given that general
enough predictive models for such noise remain elusive, crystallography tools
for APT face several limitations: Their robustness to noise, and therefore,
their capability to identify and distinguish different crystal structures and
orientation is limited. In addition, the tools are sequential and demand
substantial manual interaction. In combination, this makes robust uncertainty
quantifying with automated high-throughput studies of the latent
crystallographic information a difficult task with APT data.
To improve the situation, we review the existent methods and discuss how they
link to those in the diffraction communities. With this we modify some of the
APT methods to yield more robust descriptors of the atomic arrangement. We
report how this enables the development of an open-source software tool for
strong-scaling and automated identifying of crystal structure and mapping
crystal orientation in nanocrystalline APT datasets with multiple phases.Comment: 36 pages, 19 figures, preprin
Automatic target recognition based on cross-plot
Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository.Kelvin Kian Loong Wong and Derek Abbot
Robust signatures for 3D face registration and recognition
PhDBiometric authentication through face recognition has been an active area of
research for the last few decades, motivated by its application-driven demand. The popularity
of face recognition, compared to other biometric methods, is largely due to its
minimum requirement of subject co-operation, relative ease of data capture and similarity
to the natural way humans distinguish each other.
3D face recognition has recently received particular interest since three-dimensional
face scans eliminate or reduce important limitations of 2D face images, such as illumination
changes and pose variations. In fact, three-dimensional face scans are usually captured
by scanners through the use of a constant structured-light source, making them invariant
to environmental changes in illumination. Moreover, a single 3D scan also captures the
entire face structure and allows for accurate pose normalisation.
However, one of the biggest challenges that still remain in three-dimensional face
scans is the sensitivity to large local deformations due to, for example, facial expressions.
Due to the nature of the data, deformations bring about large changes in the 3D geometry
of the scan. In addition to this, 3D scans are also characterised by noise and artefacts such
as spikes and holes, which are uncommon with 2D images and requires a pre-processing
stage that is speci c to the scanner used to capture the data.
The aim of this thesis is to devise a face signature that is compact in size and
overcomes the above mentioned limitations. We investigate the use of facial regions and
landmarks towards a robust and compact face signature, and we study, implement and
validate a region-based and a landmark-based face signature. Combinations of regions and
landmarks are evaluated for their robustness to pose and expressions, while the matching
scheme is evaluated for its robustness to noise and data artefacts
Using diffusion distances for flexible molecular shape comparison
<p>Abstract</p> <p>Background</p> <p>Many molecules are flexible and undergo significant shape deformation as part of their function, and yet most existing molecular shape comparison (MSC) methods treat them as rigid bodies, which may lead to incorrect shape recognition.</p> <p>Results</p> <p>In this paper, we present a new shape descriptor, named Diffusion Distance Shape Descriptor (DDSD), for comparing 3D shapes of flexible molecules. The diffusion distance in our work is considered as an average length of paths connecting two landmark points on the molecular shape in a sense of inner distances. The diffusion distance is robust to flexible shape deformation, in particular to topological changes, and it reflects well the molecular structure and deformation without explicit decomposition. Our DDSD is stored as a histogram which is a probability distribution of diffusion distances between all sample point pairs on the molecular surface. Finally, the problem of flexible MSC is reduced to comparison of DDSD histograms.</p> <p>Conclusions</p> <p>We illustrate that DDSD is insensitive to shape deformation of flexible molecules and more effective at capturing molecular structures than traditional shape descriptors. The presented algorithm is robust and does not require any prior knowledge of the flexible regions.</p
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