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Warunika Ranaweera

dblp:182/9383 · DBLP profile ↗
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2ranked-venue papers
1as 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 · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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.

Computer graphics and multimedia
1 paper
Visual content generation and editing · 50% Visualization and visual analytics · 50%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › perception
text legibility
0.212016
Legible compact calligrams · ACM Trans. Graph. 2016

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

path generation · 0.2letter deformation · 0.2crowdsourced study · 0.2
YearPublicationVenuePosition
2017 ExquiMo: An Exquisite Corpse Tool for Collaborative 3D Shape Design
Warunika Ranaweera, Parmit K. Chilana, Daniel Cohen-Or, Hao (Richard) Zhang
J. Comput. Sci. Technol.1
2016 Legible compact calligrams
abstract
A calligram is an arrangement of words or letters that creates a visual image, and a compact calligram fits one word into a 2D shape. We introduce a fully automatic method for the generation of legible compact calligrams which provides a balance between conveying the input shape, legibility, and aesthetics. Our method has three key elements: a path generation step which computes a global layout path suitable for embedding the input word; an alignment step to place the letters so as to achieve feature alignment between letter and shape protrusions while maintaining word legibility; and a final deformation step which deforms the letters to fit the shape while balancing fit against letter legibility. As letter legibility is critical to the quality of compact calligrams, we conduct a large-scale crowd-sourced study on the impact of different letter deformations on legibility and use the results to train a letter legibility measure which guides the letter deformation. We show automatically generated calligrams on an extensive set of word-image combinations. The legibility and overall quality of the calligrams are evaluated and compared, via user studies, to those produced by human creators, including a professional artist, and existing works.
Changqing Zou, Junjie Cao 0001, Warunika Ranaweera, Ibraheem Alhashim, Ping Tan 0002, Alla Sheffer, Hao (Richard) Zhang
ACM Trans. Graph.3