NPAR16ISBN 978-3-03868-002-4https://diglib.eg.org:443/handle/10.2312/150692024-03-28T06:34:17Z2024-03-28T06:34:17ZData-Driven IconificationLiu, YimingAgarwala, AseemLu, JingwanRusinkiewicz, Szymonhttps://diglib.eg.org:443/handle/10.2312/exp201610702022-03-28T06:50:44Z2016-01-01T00:00:00ZData-Driven Iconification
Liu, Yiming; Agarwala, Aseem; Lu, Jingwan; Rusinkiewicz, Szymon
Pierre Bénard and Holger Winnemöller
Pictograms (icons) are ubiquitous in visual communication, but creating the best icon is not easy: users may wish to see a variety of possibilities before settling on a final form, and they might lack the ability to draw attractive and effective pictograms by themselves. We describe a system that synthesizes novel pictograms by remixing portions of icons retrieved from a large online repository. Depending on the user's needs, the synthesis can be controlled by a number of interfaces ranging from sketch-based modeling and editing to fully-automatic hybrid generation and scribble-guided montage. Our system combines icon-specific algorithms for salient-region detection, shape matching, and multi-label graph-cut stitching to produce results in styles ranging from line drawings to solid shapes with interior structure.
2016-01-01T00:00:00ZInteractive NPAR: What Type of Tools Should We Create?Isenberg, Tobiashttps://diglib.eg.org:443/handle/10.2312/exp201610672022-03-28T06:50:43Z2016-01-01T00:00:00ZInteractive NPAR: What Type of Tools Should We Create?
Isenberg, Tobias
Pierre Bénard and Holger Winnemöller
I argue that we need to increase our consideration of the interaction that is possible and/or needed for the NPAR algorithms we develop. Depending on the application domain of a given algorithmic contribution, different degrees of interaction are required to make it practically useful and, thus, relevant. The spectrum of interactivity ranges from (almost) fully automatic processing to levels of control that are similar to those of traditional tools-some of the approaches even needing to support the full spectrum. Only if these considerations are first-class members of the NPAR development process can we expect others to want to work with our tools and to use them on a regular basis.
2016-01-01T00:00:00ZMap Style Formalization: Rendering Techniques Extension for CartographyChristophe, SidonieDuménieu, BertrandTurbet, JérémieHoarau, CharlotteMellado, NicolasOry, JérémieLoi, HugoMasse, AntoineArbelot, BenoitVergne, RomainBrédif, MathieuHurtut, ThomasThollot, JoëlleVanderhaeghe, Davidhttps://diglib.eg.org:443/handle/10.2312/exp201610642022-03-28T06:50:41Z2016-01-01T00:00:00ZMap Style Formalization: Rendering Techniques Extension for Cartography
Christophe, Sidonie; Duménieu, Bertrand; Turbet, Jérémie; Hoarau, Charlotte; Mellado, Nicolas; Ory, Jérémie; Loi, Hugo; Masse, Antoine; Arbelot, Benoit; Vergne, Romain; Brédif, Mathieu; Hurtut, Thomas; Thollot, Joëlle; Vanderhaeghe, David
Pierre Bénard and Holger Winnemöller
Cartographic design requires controllable methods and tools to produce maps that are adapted to users' needs and preferences. The formalized rules and constraints for cartographic representation come mainly from the conceptual framework of graphic semiology. Most current Geographical Information Systems (GIS) rely on the Styled Layer Descriptor and Semiology Encoding (SLD/SE) specifications which provide an XML schema describing the styling rules to be applied on geographic data to draw a map. Although this formalism is relevant for most usages in cartography, it fails to describe complex cartographic and artistic styles. In order to overcome these limitations, we propose an extension of the existing SLD/SE specifications to manage extended map stylizations, by the means of controllable expressive methods. Inspired by artistic and cartographic sources (Cassini maps, mountain maps, artistic movements, etc.), we propose to integrate into our system three main expressive methods: linear stylization, patch-based region filling and vector texture generation. We demonstrate how our pipeline allows to personalize map rendering with expressive methods in several examples.
2016-01-01T00:00:00ZPatternista: Learning Element Style Compatibility and Spatial Composition for Ring-based Layout DecorationPhan, Huy QuocLu, JingwanAsente, PaulChan, Antoni B.Fu, Hongbohttps://diglib.eg.org:443/handle/10.2312/exp201610662022-03-28T06:50:43Z2016-01-01T00:00:00ZPatternista: Learning Element Style Compatibility and Spatial Composition for Ring-based Layout Decoration
Phan, Huy Quoc; Lu, Jingwan; Asente, Paul; Chan, Antoni B.; Fu, Hongbo
Pierre Bénard and Holger Winnemöller
Creating aesthetically pleasing decorations for daily objects is a task that requires deep understanding of multiple aspects of object decoration, including color, composition and element compatibility. A designer needs a unique aesthetic style to create artworks that stand out. Although specific subproblems have been studied before, the overall problem of design recommendation and synthesis is still relatively unexplored. In this paper, we propose a flexible data-driven framework to jointly consider two aspects of this design problem: style compatibility and spatial composition. We introduce a ring-based layout model capable of capturing decorative compositions for objects like plates, vases and pots. Our layout representation allows the use of the hidden Markov models (HMM's) technique to make intelligent design suggestions for each region of a target object in a sequential fashion. We conducted both quantitative and qualitative experiments to evaluate the framework and obtained favorable results.
2016-01-01T00:00:00Z