EG 2020 - Short Papers
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Browsing EG 2020 - Short Papers by Author "Cani, Marie-Paule"
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Item Interactive Flat Coloring of Minimalist Neat Sketches(The Eurographics Association, 2020) Parakkat, Amal Dev; Madipally, Prudhviraj; Gowtham, Hari Hara; Cani, Marie-Paule; Wilkie, Alexander and Banterle, FrancescoWe introduce a simple Delaunay-triangulation based algorithm for the interactive coloring of neat line-art minimalist sketches, ie. vector sketches that may include open contours. The main objective is to minimize user intervention and make interaction as natural as with the flood-fill algorithm while extending coloring to regions with open contours. In particular, we want to save the user from worrying about parameters such as stroke weight and size. Our solution works in two steps, 1) a segmentation step in which the input sketch is automatically divided into regions based on the underlying Delaunay structure and 2) the interactive grouping of neighboring regions based on user input. More precisely, a region adjacency graph is computed from the segmentation result, and is interactively partitioned based on user input to generate the final colored sketch. Results show that our method is as natural as a bucket fill tool and powerful enough to color minimalist sketches.Item Pair Correlation Functions with Free-Form Boundaries for Distribution Inpainting and Decomposition(The Eurographics Association, 2020) Nicolet, Baptiste; Ecormier-Nocca, Pierre; Memari, Pooran; Cani, Marie-Paule; Wilkie, Alexander and Banterle, FrancescoPair Correlation Functions (PCF) have been recently spreading as a reliable representation for distributions, enabling the efficient synthesis of point-sets, vector textures and object placement from examples. In this work we introduce a triangulationbased local filtering method to extend PCF-based analysis to exemplars with free-form boundaries. This makes PCF applicable to new problems such as the inpainting of missing parts in an input distribution, or the decomposition of complex, non-homogeneous distributions into a set of coherent classes, in which each category of points can be studied together with their intra and inter-class correlations.