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JellyLens: Content-Aware Adaptive Lenses

Cyprien Pindat 1, * Emmanuel Pietriga 1, 2 Olivier Chapuis 1 Claude Puech 1, 3 
* Corresponding author
1 IN-SITU - Situated interaction
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
2 MassData
Inria Chile
Abstract : Focus+context lens-based techniques smoothly integrate two levels of detail using spatial distortion to connect the magnified region and the context. Distortion guarantees visual continuity, but causes problems of interpretation and focus targeting, partly due to the fact that most techniques are based on statically-defined, regular lens shapes, that result in far-from-optimal magnification and distortion. JellyLenses dynamically adapt to the shape of the objects of interest, providing detail-in-context visualizations of higher relevance by optimizing what regions fall into the focus, context and spatially-distorted transition regions. This both improves the visibility of content in the focus region and preserves a larger part of the context region. We describe the approach and its implementation, and report on a controlled experiment that evaluates the usability of JellyLenses compared to regular fisheye lenses, showing clear performance improvements with the new technique for a multi-scale visual search task.
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Contributor : Emmanuel Pietriga Connect in order to contact the contributor
Submitted on : Friday, July 27, 2012 - 9:21:29 PM
Last modification on : Tuesday, October 25, 2022 - 4:18:59 PM



Cyprien Pindat, Emmanuel Pietriga, Olivier Chapuis, Claude Puech. JellyLens: Content-Aware Adaptive Lenses. UIST 2021 - 25th Symposium on User Interface Software and Technology, ACM, Oct 2012, Cambridge, Massachusetts, United States. pp.261-270, ⟨10.1145/2380116.2380150⟩. ⟨hal-00721574⟩



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