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Sanjeev Balakrishnan

dblp:190/2308 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 77% User interface design and tools · 23%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › geospatial visualization
cartographic visualization
0.312017
An Evaluation of Visual Search Support in Maps · IEEE Trans. Vis. Comput. Graph. 2017
Interaction techniques and input
visual search
0.312017
An Evaluation of Visual Search Support in Maps · IEEE Trans. Vis. Comput. Graph. 2017
User interface design and tools
annotation tools
0.112017
An Evaluation of Visual Search Support in Maps · IEEE Trans. Vis. Comput. Graph. 2017

Methods — techniques the papers use, named apart from their topics

eye tracking · 0.6controlled study · 0.6
YearPublicationVenuePosition
2017 An Evaluation of Visual Search Support in Maps
abstract
Visual search can be time-consuming, especially if the scene contains a large number of possibly relevant objects. An instance of this problem is present when using geographic or schematic maps with many different elements representing cities, streets, sights, and the like. Unless the map is well-known to the reader, the full map or at least large parts of it must be scanned to find the elements of interest. In this paper, we present a controlled eye-tracking study (30 participants) to compare four variants of map annotation with labels: within-image annotations, grid reference annotation, directional annotation, and miniature annotation. Within-image annotation places labels directly within the map without any further search support. Grid reference annotation corresponds to the traditional approach known from atlases. Directional annotation utilizes a label in combination with an arrow pointing in the direction of the label within the map. Miniature annotation shows a miniature grid to guide the reader to the area of the map in which the label is located. The study results show that within-image annotation is outperformed by all other annotation approaches. Best task completion times are achieved with miniature annotation. The analysis of eye-movement data reveals that participants applied significantly different visual task solution strategies for the different visual annotations.
Rudolf Netzel, Marcel Hlawatsch, Michael Burch, Sanjeev Balakrishnan, Hansjörg Schmauder, Daniel Weiskopf
IEEE Trans. Vis. Comput. Graph.4