30,416 research outputs found
Prototyping Information Visualization in 3D City Models: a Model-based Approach
When creating 3D city models, selecting relevant visualization techniques is
a particularly difficult user interface design task. A first obstacle is that
current geodata-oriented tools, e.g. ArcGIS, have limited 3D capabilities and
limited sets of visualization techniques. Another important obstacle is the
lack of unified description of information visualization techniques for 3D city
models. If many techniques have been devised for different types of data or
information (wind flows, air quality fields, historic or legal texts, etc.)
they are generally described in articles, and not really formalized. In this
paper we address the problem of visualizing information in (rich) 3D city
models by presenting a model-based approach for the rapid prototyping of
visualization techniques. We propose to represent visualization techniques as
the composition of graph transformations. We show that these transformations
can be specified with SPARQL construction operations over RDF graphs. These
specifications can then be used in a prototype generator to produce 3D scenes
that contain the 3D city model augmented with data represented using the
desired technique.Comment: Proc. of 3DGeoInfo 2014 Conference, Dubai, November 201
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Visual support for ontology learning: an experience report
Ontology learning methods aim to automate ontology
construction. They are complex methods involving several
elements such as documents, terms and concepts. During the development of an ontology learning method, as well as during its deployment, several situations occur where
understanding the relations between these elements is crucial. Our hypothesis is that visual techniques can be used to aid this understanding. To support this claim, we present a set of such complex situations and describe the visual solutions that we developed to support them
Metric Learning for Generalizing Spatial Relations to New Objects
Human-centered environments are rich with a wide variety of spatial relations
between everyday objects. For autonomous robots to operate effectively in such
environments, they should be able to reason about these relations and
generalize them to objects with different shapes and sizes. For example, having
learned to place a toy inside a basket, a robot should be able to generalize
this concept using a spoon and a cup. This requires a robot to have the
flexibility to learn arbitrary relations in a lifelong manner, making it
challenging for an expert to pre-program it with sufficient knowledge to do so
beforehand. In this paper, we address the problem of learning spatial relations
by introducing a novel method from the perspective of distance metric learning.
Our approach enables a robot to reason about the similarity between pairwise
spatial relations, thereby enabling it to use its previous knowledge when
presented with a new relation to imitate. We show how this makes it possible to
learn arbitrary spatial relations from non-expert users using a small number of
examples and in an interactive manner. Our extensive evaluation with real-world
data demonstrates the effectiveness of our method in reasoning about a
continuous spectrum of spatial relations and generalizing them to new objects.Comment: Accepted at the 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems. The new Freiburg Spatial Relations Dataset and a demo
video of our approach running on the PR-2 robot are available at our project
website: http://spatialrelations.cs.uni-freiburg.d
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