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The segmentation of volumetric datasets, i.e., the partitioning of the data into disjoint sub-volumes with the goal to extract information about these regions,is a difficult problem and has been discussed in medical imaging for decades.
Due to the ever-increasing imaging capabilities, in particular in X-ray computed tomography (CT) or magnetic resonance imaging, segmentation in industrial applications also gains interest.
Especially in industrial applications the generated datasets increase in size.
Hence, most applications apply well-known techniques in a 2+1-dimensional manner,i.e., they apply image segmentation procedures on each slice separately and track the progress along the axis of the volume in which the slices are stacked on.
This discards the information on preceding or subsequent slices, which is often assumed to be nearly identical. However, in the industrial context this might prove wrong since industrial parts might change their appearance significantly over the course of even a few slices.
Moreover, artifacts can further distort the content of the slices.
Therefore, three-dimensional processing of voxel volumes has to be preferred, which induces constraints upon the segmentation procedures. For example, they must not consider global information as it is usually not feasible in big scans to compute them efficiently.
Yet another frequent problem is that applications focus on individual parts only and algorithms are tailored to that case. Most prominent medical segmentation procedures do so by applying methods to specifically find the liver and only the liver of a patient, for example.
The implication is that the same method then cannot be applied to find other parts of the scan and such methods have to be designed individually for any object to be segmented.
Flexible segmentation methods are needed too specifically when partitioning unique scans. We define a unique scan to be a voxel dataset for which no comparable volume exists.
Classical examples include the use case of cultural heritage where not only the objects themselves are unique but also scan parameters are optimized to obtain the best image quality possible for that specific scan.
This thesis aims at introducing novel methods for voxelwise classifications based on local geometric features.
The latter are computed from local environments around each voxel and extract information in similar ways as humans do, namely by observing their similarity to geometric or textural primitives.
These features serve as the foundation to learning the proposed voxelwise classifiers and to discriminate between segmented and unsegmented voxels.
On the one hand, they perform fully automated clustering of volumes for which a representative random sample is extracted first.
On the other hand, a set of segmenting classifiers can be trained from few seed voxels, i.e., volume elements for which a domain expert marked if they belong to the components that shall be segmented. The interactive selection offers the advantage that no completely labeled voxel volumes are necessary and hence that unique scans of objects can be segmented for which no comparable scans exist.
Overall, it will be shown that all proposed segmentation methods are effectively of linear runtime with respect to the number of voxels in the volume. Thus, voxel volumes without size restrictions can be segmented in an efficient linear pass through the volume.
Finally, the segmentation performance is evaluated on selected datasets which shows that the introduced methods can achieve good results on scans from a broad variety of domains for both small and big voxel volumes.
In this thesis a new approach to building product recommender systems is introduced. By using a customer-centric dialogue, the customers' preferences are elicited. These are the basis for inferring utility estimations about the desired technical properties of the products in question. Systems built this way can both operate autonomously, e.g., in an online store, and support a salesperson directly at the point-of-sale. The core of the approach is formed by a layered domain description that models customer stereotypes and needs, product attributes, the products themselves, and the causal interrelations between customer and product properties. Maintenance of the domain description, i.e., keeping the model up-to-date in face of frequent changes, is facilitated by the clear separation of concerns provided by the layered structure. In fact, the most frequently used class of updates can be handled in an entirely automated way if some constraints are satisfied. On a high level of abstraction, the system behavior is described by State Charts that are parameterized according to the domain description. Those parts of the system description where State Charts would be too imprecise are implemented by separate components realizing the required complex semantics. From the domain description, a Bayesian network is generated that forms the core of the inference engine of the recommender system. The network essentially controls the system-initiated dialogue flow and the recommendation process. Due to the characteristics of Bayesian networks, it is possible to respond to user-initiated dialogue steps in a natural way. Moreover, an explanation of the current recommendation can be generated without having to explicitly encode additional information in the modeling layer. Finally, a database structure and the SQL queries necessary to obtain recommendations can be inferred from the corresponding parts of the domain description. Instantiation of the system to a specific business domain is supported by a dedicated maintenance application that hides the complexities of the underlying algorithms. Thus, day-to-day system updates by non-technical domain experts, e.g., product managers, are facilitated. The developed concepts were implemented in cooperation with a local industry partner who intends to apply the recommender system in the field of mobile communications.