10,227 research outputs found
Engineering Art Galleries
The Art Gallery Problem is one of the most well-known problems in
Computational Geometry, with a rich history in the study of algorithms,
complexity, and variants. Recently there has been a surge in experimental work
on the problem. In this survey, we describe this work, show the chronology of
developments, and compare current algorithms, including two unpublished
versions, in an exhaustive experiment. Furthermore, we show what core
algorithmic ingredients have led to recent successes
Complexity of the General Chromatic Art Gallery Problem
In the original Art Gallery Problem (AGP), one seeks the minimum number of
guards required to cover a polygon . We consider the Chromatic AGP (CAGP),
where the guards are colored. As long as is completely covered, the number
of guards does not matter, but guards with overlapping visibility regions must
have different colors. This problem has applications in landmark-based mobile
robot navigation: Guards are landmarks, which have to be distinguishable (hence
the colors), and are used to encode motion primitives, \eg, "move towards the
red landmark". Let , the chromatic number of , denote the minimum
number of colors required to color any guard cover of . We show that
determining, whether is \NP-hard for all . Keeping
the number of colors minimal is of great interest for robot navigation, because
less types of landmarks lead to cheaper and more reliable recognition
Facets for Art Gallery Problems
The Art Gallery Problem (AGP) asks for placing a minimum number of stationary
guards in a polygonal region P, such that all points in P are guarded. The
problem is known to be NP-hard, and its inherent continuous structure (with
both the set of points that need to be guarded and the set of points that can
be used for guarding being uncountably infinite) makes it difficult to apply a
straightforward formulation as an Integer Linear Program. We use an iterative
primal-dual relaxation approach for solving AGP instances to optimality. At
each stage, a pair of LP relaxations for a finite candidate subset of primal
covering and dual packing constraints and variables is considered; these
correspond to possible guard positions and points that are to be guarded.
Particularly useful are cutting planes for eliminating fractional solutions.
We identify two classes of facets, based on Edge Cover and Set Cover (SC)
inequalities. Solving the separation problem for the latter is NP-complete, but
exploiting the underlying geometric structure, we show that large subclasses of
fractional SC solutions cannot occur for the AGP. This allows us to separate
the relevant subset of facets in polynomial time. We also characterize all
facets for finite AGP relaxations with coefficients in {0, 1, 2}.
Finally, we demonstrate the practical usefulness of our approach. Our cutting
plane technique yields a significant improvement in terms of speed and solution
quality due to considerably reduced integrality gaps as compared to the
approach by Kr\"oller et al.Comment: 29 pages, 18 figures, 1 tabl
Subclass Discriminant Analysis of Morphological and Textural Features for HEp-2 Staining Pattern Classification
Classifying HEp-2 fluorescence patterns in Indirect Immunofluorescence (IIF) HEp-2 cell imaging is important for the differential diagnosis of autoimmune diseases. The current technique, based on human visual inspection, is time-consuming, subjective and dependent on the operator's experience. Automating this process may be a solution to these limitations, making IIF faster and more reliable. This work proposes a classification approach based on Subclass Discriminant Analysis (SDA), a dimensionality reduction technique that provides an effective representation of the cells in the feature space, suitably coping with the high within-class variance typical of HEp-2 cell patterns. In order to generate an adequate characterization of the fluorescence patterns, we investigate the individual and combined contributions of several image attributes, showing that the integration of morphological, global and local textural features is the most suited for this purpose. The proposed approach provides an accuracy of the staining pattern classification of about 90%
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