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When developing new ICT systems and applications for domestic environments, rich qualitative approaches improve the understanding of the user's integral usage of technology in their daily routines and thereby inform design. This knowledge will often be reached through in-home studies, strong relationships with the users and their involvement in the design and evaluation process. However, whilst this kind of research offers valuable context insights and brings out unexpected findings, it also presents methodological, technical and organizational challenges for the study design and its underlying cooperation processes. In particular, due to heterogeneous users in households in terms of technology affinity, individual needs, age distribution, gender, social constellations, personal role assignment, project expectations, etc. it produces particular demands to collaborate with users in the design process and thereby exposes a range of practical challenges. The full-day workshop wishes to identify these practical challenges, discuss best practice and develop a roadmap for sustainable relationships for design with users.
The title of the annual report 2013 "Shaping change: The University Addresses Society‘s Probing Challenges" reveals the great importance placed on social, economic and technological changes at the university.
This key aspect thus runs through the contents of the 90-page annual report like a common thread, without losing track of the enormous variety of research and teaching at Bonn-Rhein-Sieg University. Whether the exploration of gaps in robot safety during a European Intensive Programme, a report about the Philipines crisis region from a graduate who has worked as an organizer for Care International, or the chapter "What does change look like?" – The annual report provides the full spectrum of opportunities, activities and findings of university members.
A principal step towards solving diverse perception problems is segmentation. Many algorithms benefit from initially partitioning input point clouds into objects and their parts. In accordance with cognitive sciences, segmentation goal may be formulated as to split point clouds into locally smooth convex areas, enclosed by sharp concave boundaries. This goal is based on purely geometrical considerations and does not incorporate any constraints, or semantics, of the scene and objects being segmented, which makes it very general and widely applicable. In this work we perform geometrical segmentation of point cloud data according to the stated goal. The data is mapped onto a graph and the task of graph partitioning is considered. We formulate an objective function and derive a discrete optimization problem based on it. Finding the globally optimal solution is an NP-complete problem; in order to circumvent this, spectral methods are applied. Two algorithms that implement the divisive hierarchical clustering scheme are proposed. They derive graph partition by analyzing the eigenvectors obtained through spectral relaxation. The specifics of our application domain are used to automatically introduce cannot-link constraints in the clustering problem. The algorithms function in completely unsupervised manner and make no assumptions about shapes of objects and structures that they segment. Three publicly available datasets with cluttered real-world scenes and an abundance of box-like, cylindrical, and free-form objects are used to demonstrate convincing performance. Preliminary results of this thesis have been contributed to the International Conference on Autonomous Intelligent Systems (IAS-13).