2,696 research outputs found
A survey on algorithmic aspects of modular decomposition
The modular decomposition is a technique that applies but is not restricted
to graphs. The notion of module naturally appears in the proofs of many graph
theoretical theorems. Computing the modular decomposition tree is an important
preprocessing step to solve a large number of combinatorial optimization
problems. Since the first polynomial time algorithm in the early 70's, the
algorithmic of the modular decomposition has known an important development.
This paper survey the ideas and techniques that arose from this line of
research
Letter graphs and geometric grid classes of permutations: characterization and recognition
In this paper, we reveal an intriguing relationship between two seemingly
unrelated notions: letter graphs and geometric grid classes of permutations. An
important property common for both of them is well-quasi-orderability,
implying, in a non-constructive way, a polynomial-time recognition of geometric
grid classes of permutations and -letter graphs for a fixed . However,
constructive algorithms are available only for . In this paper, we present
the first constructive polynomial-time algorithm for the recognition of
-letter graphs. It is based on a structural characterization of graphs in
this class.Comment: arXiv admin note: text overlap with arXiv:1108.6319 by other author
Between Subgraph Isomorphism and Maximum Common Subgraph
When a small pattern graph does not occur inside a larger target graph, we can ask how to find "as much of the pattern as possible" inside the target graph. In general, this is known as the maximum common subgraph problem, which is much more computationally challenging in practice than subgraph isomorphism. We introduce a restricted alternative, where we ask if all but k vertices from the pattern can be found in the target graph. This allows for the development of slightly weakened forms of certain invariants from subgraph isomorphism which are based upon degree and number of paths. We show that when k is small, weakening the invariants still retains much of their effectiveness. We are then able to solve this problem on the standard problem instances used to benchmark subgraph isomorphism algorithms, despite these instances being too large for current maximum common subgraph algorithms to handle. Finally, by iteratively increasing k, we obtain an algorithm which is also competitive for the maximum common subgraph
Unit Grid Intersection Graphs: Recognition and Properties
It has been known since 1991 that the problem of recognizing grid
intersection graphs is NP-complete. Here we use a modified argument of the
above result to show that even if we restrict to the class of unit grid
intersection graphs (UGIGs), the recognition remains hard, as well as for all
graph classes contained inbetween. The result holds even when considering only
graphs with arbitrarily large girth. Furthermore, we ask the question of
representing UGIGs on grids of minimal size. We show that the UGIGs that can be
represented in a square of side length 1+epsilon, for a positive epsilon no
greater than 1, are exactly the orthogonal ray graphs, and that there exist
families of trees that need an arbitrarily large grid
DOPE: Distributed Optimization for Pairwise Energies
We formulate an Alternating Direction Method of Mul-tipliers (ADMM) that
systematically distributes the computations of any technique for optimizing
pairwise functions, including non-submodular potentials. Such discrete
functions are very useful in segmentation and a breadth of other vision
problems. Our method decomposes the problem into a large set of small
sub-problems, each involving a sub-region of the image domain, which can be
solved in parallel. We achieve consistency between the sub-problems through a
novel constraint that can be used for a large class of pair-wise functions. We
give an iterative numerical solution that alternates between solving the
sub-problems and updating consistency variables, until convergence. We report
comprehensive experiments, which demonstrate the benefit of our general
distributed solution in the case of the popular serial algorithm of Boykov and
Kolmogorov (BK algorithm) and, also, in the context of non-submodular
functions.Comment: Accepted at CVPR 201
FO Model Checking of Geometric Graphs
Over the past two decades the main focus of research into first-order (FO)
model checking algorithms has been on sparse relational structures -
culminating in the FPT algorithm by Grohe, Kreutzer and Siebertz for FO model
checking of nowhere dense classes of graphs. On contrary to that, except the
case of locally bounded clique-width only little is currently known about FO
model checking of dense classes of graphs or other structures. We study the FO
model checking problem for dense graph classes definable by geometric means
(intersection and visibility graphs). We obtain new nontrivial FPT results,
e.g., for restricted subclasses of circular-arc, circle, box, disk, and
polygon-visibility graphs. These results use the FPT algorithm by Gajarsk\'y et
al. for FO model checking of posets of bounded width. We also complement the
tractability results by related hardness reductions
A strategy for the visual recognition of objects in an industrial environment.
This thesis is concerned with the problem of recognizing industrial
objects rapidly and flexibly. The system design is based on a
general strategy that consists of a generalized local feature detector,
an extended learning algorithm and the use of unique structure of
the objects. Thus, the system is not designed to be limited to the
industrial environment.
The generalized local feature detector uses the gradient image of
the scene to provide a feature description that is insensitive to a
range of imaging conditions such as object position, and overall light
intensity. The feature detector is based on a representative point
algorithm which is able to reduce the data content of the image
without restricting the allowed object geometry. Thus, a major advantage
of the local feature detector is its ability to describe and
represent complex object structure. The reliance on local features
also allows the system to recognize partially visible objects.
The task of the learning algorithm is to observe the feature
description generated by the feature detector in order to select
features that are reliable over the range of imaging conditions of
interest. Once a set of reliable features is found for each object,
the system finds unique relational structure which is later used to
recognize the objects. Unique structure is a set of descriptions of
unique subparts of the objects of interest. The present implementation
is limited to the use of unique local structure. The recognition
routine uses these unique descriptions to recognize objects in new
images. An important feature of this strategy is the transference of
a large amount of processing required for graph matching from the
recognition stage to the learning stage, which allows the recognition
routine to execute rapidly.
The test results show that the system is able to function with a
significant level of insensitivity to operating conditions; The system
shows insensitivity to its 3 main assumptions -constant scale, constant
lighting, and 2D images- displaying a degree of graceful degradation
when the operating conditions degrade. For example, for one
set of test objects, the recognition threshold was reached when the
absolute light level was reduced by 70%-80%, or the object scale was
reduced by 30%-40%, or the object was tilted away from the learned 2D
plane by 300-400. This demonstrates a very important feature of the
learning strategy: It shows that the generalizations made by the system
are not only valid within the domain of the sampled set of images,
but extend outside this domain. The test results also show that the
recognition routine is able to execute rapidly, requiring 10ms-500ms
(on a PDP11/24 minicomputer) in the special case when ideal operating
conditions are guaranteed. (Note: This does not include pre-processing
time). This thesis describes the strategy, the architecture and the
implementation of the vision system in detail, and gives detailed test
results. A proposal for extending the system to scale independent 3D
object recognition is also given
Claw-free t-perfect graphs can be recognised in polynomial time
A graph is called t-perfect if its stable set polytope is defined by
non-negativity, edge and odd-cycle inequalities. We show that it can be decided
in polynomial time whether a given claw-free graph is t-perfect
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