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Item Patternista: Learning Element Style Compatibility and Spatial Composition for Ring-based Layout Decoration(The Eurographics Association, 2016) Phan, Huy Quoc; Lu, Jingwan; Asente, Paul; Chan, Antoni B.; Fu, Hongbo; Pierre Bénard and Holger WinnemöllerCreating 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.Item Data-Driven Iconification(The Eurographics Association, 2016) Liu, Yiming; Agarwala, Aseem; Lu, Jingwan; Rusinkiewicz, Szymon; Pierre Bénard and Holger WinnemöllerPictograms (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.