A benchmark set for the reconstruction of hv-convex discrete sets

AbstractIn this paper we summarize the most important generation methods developed for the subclasses of hv-convex discrete sets. We also present some new generation techniques to complement the former ones thus making it possible to design a complete benchmark set for testing the performance of reconstruction algorithms on the class of hv-convex discrete sets and its subclasses. By using this benchmark set the paper also collects several statistics on hv-convex discrete sets, which are of great importance in the analysis of algorithms for reconstructing such kinds of discrete sets

Reconstruction of Convex Sets from One or Two X-rays

We consider a class of problems of Discrete Tomography which has been deeply investigated in the past: the reconstruction of convex lattice sets from their horizontal and/or vertical X-rays, i.e. from the number of points in a sequence of consecutive horizontal and vertical lines. The reconstruction of the HV-convex polyominoes works usually in two steps, first the filling step consisting in filling operations, second the convex aggregation of the switching components. We prove three results about the convex aggregation step: (1) The convex aggregation step used for the reconstruction of HV-convex polyominoes does not always provide a solution. The example yielding to this result is called \textit{the bad guy} and disproves a conjecture of the domain. (2) The reconstruction of a digital convex lattice set from only one X-ray can be performed in polynomial time. We prove it by encoding the convex aggregation problem in a Directed Acyclic Graph. (3) With the same strategy, we prove that the reconstruction of fat digital convex sets from their horizontal and vertical X-rays can be solved in polynomial time. Fatness is a property of the digital convex sets regarding the relative position of the left, right, top and bottom points of the set. The complexity of the reconstruction of the lattice sets which are not fat remains an open question.Comment: 31 pages, 24 figure

A framework for generating some discrete sets with disjoint components by using uniform distributions

AbstractDiscrete tomography deals with the reconstruction of discrete sets from few projections. Assuming that the set to be reconstructed belongs to a certain class of discrete sets with some geometrical properties is a commonly used technique to reduce the number of possibly many different solutions of the same reconstruction problem. The average performance of reconstruction algorithms are often tested on such classes by choosing elements of a given class from uniform random distributions. This paper presents a general framework for generating discrete sets with disjoint connected components using uniform distributions. Especially, the uniform random generation of hv-convex discrete sets and Q-convex discrete sets according to the size of the minimal bounding rectangle are discussed

Algorithms for visualization of graph-based structures

Buildings today are built to maintain a healthy indoor environment and an efficient energy usage which is probably why damages caused by dampness has increased since the 1960’s. A study between year 2008 and 2010 showed that 26 percent of the 110 000 examined houses had damages and flaws caused by dampness that could prove to be harmful later on. This means that one out of four bathrooms risk the chance to develop damages by dampness. Approximately 2 percent of the houses had already developed water damages. It is here where the problems appear. A house or a building that is damaged by water of dampness need time to dry out before any renovation can take place. This means that damaged parts must be removed and allowed to dry out, this takes a long time to do and the costs are high and at the same time it can cause inconvenience to the residents. Here is where the Air Gap Method enters the picture. The meaning with the method is to drain and dry out the moisture without the need to perform a larger renovation. The Air Gap Method is a so called "forgiving"-system that is if water damages occur the consequences will be small. The Air Gap method means that an air gap is created in the walls, ceiling and the floor where a heating cable in the gap heats up the air and creates an air movement. The point is to create a stack effect in the gap that with the help of the air movement transports the damp air through an opening by the ceiling. The aim of this thesis is to examine if it’s necessary with the heating cable in the air gap and if there is a specific drying out pattern of the water damaged bathroom floor. The possibility of mould growth will also be examined. The study showed that the damped floor did dry out even without a heating cable, but as one of the studies showed signs of mould growth it is shown that the risk for mould growth is higher without a heating cable. There was a seven days difference in the drying out time between the studies with and without the heating cable; this difference can be decisive for mould growth which is why the heating cable is recommended. The Air Gap method is quite easy to apply in houses with light frame constructions simply by using a smaller dimension on the studs to create the air gap in the floor and walls. The method can also be applied in apartment buildings with a concrete frame by using the room-in- room principal. When renovating existing bathrooms it’s easier to use prefabricated elements to create the air gap in the floor and walls. ~

Network Flow Algorithms for Discrete Tomography

Tomography is a powerful technique to obtain images of the interior of an object in a nondestructive way. First, a series of projection images (e.g., X-ray images) is acquired and subsequently a reconstruction of the interior is computed from the available project data. The algorithms that are used to compute such reconstructions are known as tomographic reconstruction algorithms. Discrete tomography is concerned with the tomographic reconstruction of images that are known to contain only a few different gray levels. By using this knowledge in the reconstruction algorithm it is often possible to reduce the number of projections required to compute an accurate reconstruction, compared to algorithms that do not use prior knowledge. This thesis deals with new reconstruction algorithms for discrete tomography. In particular, the first five chapters are about reconstruction algorithms based on network flow methods. These algorithms make use of an elegant correspondence between certain types of tomography problems and network flow problems from the field of Operations Research. Chapter 6 deals with a problem that occurs in the application of discrete tomography to the reconstruction of nanocrystals from projections obtained by electron microscopy.The research for this thesis has been financially supported by the Netherlands Organisation for Scientific Research (NWO), project 613.000.112.UBL - phd migration 201