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Sadia Rubab

dblp:231/6737 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · conflict

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

Artificial intelligence and machine learning · 1Graphics, 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.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 70% Multimedia systems and quality of experience · 30%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
authoring tool
0.412019
iStoryline: Effective Convergence to Hand-drawn Storylines · IEEE Trans. Vis. Comput. Graph. 2019
Visualization and visual analytics › data storytelling
storyline visualization
0.412019
iStoryline: Effective Convergence to Hand-drawn Storylines · IEEE Trans. Vis. Comput. Graph. 2019
Multimedia systems and quality of experience
user interaction
0.412019
iStoryline: Effective Convergence to Hand-drawn Storylines · IEEE Trans. Vis. Comput. Graph. 2019

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

optimization algorithm · 0.4
YearPublicationVenuePosition
2020 Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection
Muhammad Attique Khan, Sadia Rubab, Asifa Kashif, Muhammad Sharif 0001, Muhammad Nazeer, Jamal Hussain Shah, Yudong Zhang 0001, Suresh Chandra Satapathy
Pattern Recognit. Lett.2
2019 iStoryline: Effective Convergence to Hand-drawn Storylines
abstract
Storyline visualization techniques have progressed significantly to generate illustrations of complex stories automatically. However, the visual layouts of storylines are not enhanced accordingly despite the improvement in the performance and extension of its application area. Existing methods attempt to achieve several shared optimization goals, such as reducing empty space and minimizing line crossings and wiggles. However, these goals do not always produce optimal results when compared to hand-drawn storylines. We conducted a preliminary study to learn how users translate a narrative into a hand-drawn storyline and check whether the visual elements in hand-drawn illustrations can be mapped back to appropriate narrative contexts. We also compared the hand-drawn storylines with storylines generated by the state-of-the-art methods and found they have significant differences. Our findings led to a design space that summarizes 1) how artists utilize narrative elements and 2) the sequence of actions artists follow to portray expressive and attractive storylines. We developed iStoryline, an authoring tool for integrating high-level user interactions into optimization algorithms and achieving a balance between hand-drawn storylines and automatic layouts. iStoryline allows users to create novel storyline visualizations easily according to their preferences by modifying the automatically generated layouts. The effectiveness and usability of iStoryline are studied with qualitative evaluations.
Tan Tang, Sadia Rubab, Jiewen Lai, Weiwei Cui 0001, Lingyun Yu 0001, Yingcai Wu
IEEE Trans. Vis. Comput. Graph.2