12,486 research outputs found
How to Extract the Geometry and Topology from Very Large 3D Segmentations
Segmentation is often an essential intermediate step in image analysis. A
volume segmentation characterizes the underlying volume image in terms of
geometric information--segments, faces between segments, curves in which
several faces meet--as well as a topology on these objects. Existing algorithms
encode this information in designated data structures, but require that these
data structures fit entirely in Random Access Memory (RAM). Today, 3D images
with several billion voxels are acquired, e.g. in structural neurobiology.
Since these large volumes can no longer be processed with existing methods, we
present a new algorithm which performs geometry and topology extraction with a
runtime linear in the number of voxels and log-linear in the number of faces
and curves. The parallelizable algorithm proceeds in a block-wise fashion and
constructs a consistent representation of the entire volume image on the hard
drive, making the structure of very large volume segmentations accessible to
image analysis. The parallelized C++ source code, free command line tools and
MATLAB mex files are avilable from
http://hci.iwr.uni-heidelberg.de/software.phpComment: C++ source code, free command line tools and MATLAB mex files are
avilable from http://hci.iwr.uni-heidelberg.de/software.ph
Generating Second Order (Co)homological Information within AT-Model Context
In this paper we design a new family of relations between
(co)homology classes, working with coefficients in a field and starting
from an AT-model (Algebraic Topological Model) AT(C) of a finite cell
complex C These relations are induced by elementary relations of type
“to be in the (co)boundary of” between cells. This high-order connectivity
information is embedded into a graph-based representation model,
called Second Order AT-Region-Incidence Graph (or AT-RIG) of C. This
graph, having as nodes the different homology classes of C, is in turn,
computed from two generalized abstract cell complexes, called primal
and dual AT-segmentations of C. The respective cells of these two complexes
are connected regions (set of cells) of the original cell complex C,
which are specified by the integral operator of AT(C). In this work in
progress, we successfully use this model (a) in experiments for discriminating
topologically different 3D digital objects, having the same Euler
characteristic and (b) in designing a parallel algorithm for computing
potentially significant (co)homological information of 3D digital objects.Ministerio de Economía y Competitividad MTM2016-81030-PMinisterio de Economía y Competitividad TEC2012-37868-C04-0
Analysis of Three-Dimensional Protein Images
A fundamental goal of research in molecular biology is to understand protein
structure. Protein crystallography is currently the most successful method for
determining the three-dimensional (3D) conformation of a protein, yet it
remains labor intensive and relies on an expert's ability to derive and
evaluate a protein scene model. In this paper, the problem of protein structure
determination is formulated as an exercise in scene analysis. A computational
methodology is presented in which a 3D image of a protein is segmented into a
graph of critical points. Bayesian and certainty factor approaches are
described and used to analyze critical point graphs and identify meaningful
substructures, such as alpha-helices and beta-sheets. Results of applying the
methodologies to protein images at low and medium resolution are reported. The
research is related to approaches to representation, segmentation and
classification in vision, as well as to top-down approaches to protein
structure prediction.Comment: See http://www.jair.org/ for any accompanying file
Contains and Inside relationships within combinatorial Pyramids
Irregular pyramids are made of a stack of successively reduced graphs
embedded in the plane. Such pyramids are used within the segmentation framework
to encode a hierarchy of partitions. The different graph models used within the
irregular pyramid framework encode different types of relationships between
regions. This paper compares different graph models used within the irregular
pyramid framework according to a set of relationships between regions. We also
define a new algorithm based on a pyramid of combinatorial maps which allows to
determine if one region contains the other using only local calculus.Comment: 35 page
Task-based Augmented Contour Trees with Fibonacci Heaps
This paper presents a new algorithm for the fast, shared memory, multi-core
computation of augmented contour trees on triangulations. In contrast to most
existing parallel algorithms our technique computes augmented trees, enabling
the full extent of contour tree based applications including data segmentation.
Our approach completely revisits the traditional, sequential contour tree
algorithm to re-formulate all the steps of the computation as a set of
independent local tasks. This includes a new computation procedure based on
Fibonacci heaps for the join and split trees, two intermediate data structures
used to compute the contour tree, whose constructions are efficiently carried
out concurrently thanks to the dynamic scheduling of task parallelism. We also
introduce a new parallel algorithm for the combination of these two trees into
the output global contour tree. Overall, this results in superior time
performance in practice, both in sequential and in parallel thanks to the
OpenMP task runtime. We report performance numbers that compare our approach to
reference sequential and multi-threaded implementations for the computation of
augmented merge and contour trees. These experiments demonstrate the run-time
efficiency of our approach and its scalability on common workstations. We
demonstrate the utility of our approach in data segmentation applications
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