Event Entry
What and Who
Pattern Search for Visualization
Date, Time and Location
Abstract
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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