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We introduce an approach for converting pixel art into high-quality vector images. While much progress has been made on automatic conversion, there is an inherent ambiguity in pixel art, which can lead to a mismatch with the artist's original intent. Further, there is room for incorporating aesthetic preferences during the conversion. In consequence, this work introduces an interactive framework to enable users to guide the conversion process towards high-quality vector illustrations. A key idea of the method is to cast the conversion process into a spring-system optimization that can be influenced by the user. Hereby, it is possible to resolve various ambiguities that cannot be handled by an automatic algorithm.

We introduce Delaunay Painting, a novel and easy-to-use method to flat-color contour-sketches with gaps. Starting from a Delaunay triangulation of the input contours, triangles are iteratively filled with the appropriate colors, thanks to the dynamic update of flow values calculated from color hints. Aesthetic finish is then achieved, through energy minimisation of contour curves and further heuristics enforcing the appropriate sharp corners. To be more efficient, the user can also make use of our color diffusion framework which automatically extends coloring to small, internal regions such as those delimited by hatches. The resulting method robustly handles input contours with strong gaps. As an interactive tool, it minimizes user's efforts and enables any coloring strategy, as the result does not depend on the order of interactions. We also provide an automatized version of the coloring strategy for quick segmentation of contours images, that we illustrate with an application to medical imaging.

We 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., Eurographics 2020 - Short Papers, Visualisation / NPR, 85, 88, Amal Dev Parakkat, Prudhviraj Madipally, Hari Hara Gowtham, and Marie-Paule Cani, CCS Concepts: Theory of computation --> Computational geometry; Computing methodologies --> Image processing; Shape analysis; Applied computing --> Fine arts

Given a set of points S ∈ R 2 , reconstruction is a process of identifying the boundary edges that best approximates the set of points. In this paper, we propose a unified algorithm for reconstruction that works for both dot patterns as well as boundary samples. The algorithm starts with computing the Delaunay triangulation of the given point set and edges are iteratively removed based on the structure of a pair of triangles. Further, we also propose additional criteria for removing edges based on characterizing a triangle and using degree constraint. Unlike the existing algorithms, the proposed approach requires only a single pass to capture both inner and outer boundaries irrespective of the number of objects/holes. Moreover, the same criterion has been employed for both inner and outer boundary detection. The experiments show that our approach works well for different kinds of inputs. We have done extensive comparisons with state-of-the-art methods for various kinds of point sets including varying the sampling density and distribution and found to perform better or on par with them.

In this work, we introduce a dedicated variant of traditional 2D reconstruction in which the input point set (a stippled image) caninclude different regions corresponding to different point densities. Our method converts this input into a layered, vector representation, which includes a main shape plus internal, closed regions, defined by their boundary. Providing such structured output eases subsequent editing and processing, such as generating shaded vector images from the stippled input. To achieve this, we first apply a layered reconstruction algorithm based on the detectedregions in the input point set, and use the output for generating the outer and inner shape boundaries in the stippled drawing