16,463 research outputs found
Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup
Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the clataset, the specific tools used and the 'rnashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here
Investigating the effectiveness of an efficient label placement method using eye movement data
This paper focuses on improving the efficiency and effectiveness of dynamic and interactive maps in relation to the user. A label placement method with an improved algorithmic efficiency is presented. Since this algorithm has an influence on the actual placement of the name labels on the map, it is tested if this efficient algorithms also creates more effective maps: how well is the information processed by the user. We tested 30 participants while they were working on a dynamic and interactive map display. Their task was to locate geographical names on each of the presented maps. Their eye movements were registered together with the time at which a given label was found. The gathered data reveal no difference in the user's response times, neither in the number and the duration of the fixations between both map designs. The results of this study show that the efficiency of label placement algorithms can be improved without disturbing the user's cognitive map. Consequently, we created a more efficient map without affecting its effectiveness towards the user
Robot Navigation in Unseen Spaces using an Abstract Map
Human navigation in built environments depends on symbolic spatial
information which has unrealised potential to enhance robot navigation
capabilities. Information sources such as labels, signs, maps, planners, spoken
directions, and navigational gestures communicate a wealth of spatial
information to the navigators of built environments; a wealth of information
that robots typically ignore. We present a robot navigation system that uses
the same symbolic spatial information employed by humans to purposefully
navigate in unseen built environments with a level of performance comparable to
humans. The navigation system uses a novel data structure called the abstract
map to imagine malleable spatial models for unseen spaces from spatial symbols.
Sensorimotor perceptions from a robot are then employed to provide purposeful
navigation to symbolic goal locations in the unseen environment. We show how a
dynamic system can be used to create malleable spatial models for the abstract
map, and provide an open source implementation to encourage future work in the
area of symbolic navigation. Symbolic navigation performance of humans and a
robot is evaluated in a real-world built environment. The paper concludes with
a qualitative analysis of human navigation strategies, providing further
insights into how the symbolic navigation capabilities of robots in unseen
built environments can be improved in the future.Comment: 15 pages, published in IEEE Transactions on Cognitive and
Developmental Systems (http://doi.org/10.1109/TCDS.2020.2993855), see
https://btalb.github.io/abstract_map/ for access to softwar
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Semantic 3D Reconstruction with Finite Element Bases
We propose a novel framework for the discretisation of multi-label problems
on arbitrary, continuous domains. Our work bridges the gap between general FEM
discretisations, and labeling problems that arise in a variety of computer
vision tasks, including for instance those derived from the generalised Potts
model. Starting from the popular formulation of labeling as a convex relaxation
by functional lifting, we show that FEM discretisation is valid for the most
general case, where the regulariser is anisotropic and non-metric. While our
findings are generic and applicable to different vision problems, we
demonstrate their practical implementation in the context of semantic 3D
reconstruction, where such regularisers have proved particularly beneficial.
The proposed FEM approach leads to a smaller memory footprint as well as faster
computation, and it constitutes a very simple way to enable variable, adaptive
resolution within the same model
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