Pattern Search for the Visualization of Scalar, Vector, and Line Fields
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2015-12-18
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Abstract
The main topic of this thesis is pattern search in data sets for the purpose of visual
data analysis. By giving a reference pattern, pattern search aims to discover similar
occurrences in a data set with invariance to translation, rotation and scaling. To address
this problem, we developed algorithms dealing with different types of data: scalar fields,
vector fields, and line fields.
For scalar fields, we use the SIFT algorithm (Scale-Invariant Feature Transform) to
find a sparse sampling of prominent features in the data with invariance to translation,
rotation, and scaling. Then, the user can define a pattern as a set of SIFT features by
e.g. brushing a region of interest. Finally, we locate and rank matching patterns in the
entire data set. Due to the sparsity and accuracy of SIFT features, we achieve fast and
memory-saving pattern query in large scale scalar fields.
For vector fields, we propose a hashing strategy in scale space to accelerate the
convolution-based pattern query. We encode the local flow behavior in scale space using
a sequence of hierarchical base descriptors, which are pre-computed and hashed into a
number of hash tables. This ensures a fast fetching of similar occurrences in the flow
and requires only a constant number of table lookups.
For line fields, we present a stream line segmentation algorithm to split long stream
lines into globally-consistent segments, which provides similar segmentations for similar
flow structures. It gives the benefit of isolating a pattern from long and dense stream
lines, so that our patterns can be defined sparsely and have a significant extent, i.e.,
they are integration-based and not local. This allows for a greater flexibility in defining
features of interest. For user-defined patterns of curve segments, our algorithm finds
similar ones that are invariant to similarity transformations.
Additionally, we present a method for shape recovery from multiple views. This
semi-automatic method fits a template mesh to high-resolution normal data. In contrast
to existing 3D reconstruction approaches, we accelerate the data acquisition time by
omitting the structured light scanning step of obtaining low frequency 3D information.
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