Hand drawn sketch drawings to vector graphics

Abstract

Computer Numerical Control (CNC) machines and 3D printers are becoming more accessible. This allows artists and consumers to create physical objects from their drawn ideas. However, many artists are more familiar with the traditional method of sketching which uses raster format. For these kind of devices, a special instruction set based on vector notations is used which requires specialised software. One problem is that learning a new interface or technology for 3D modelling is not trivial and some might give up before trying. A bridge that easily connects these two worlds would be beneficial to both. However, even though the interpretation of sketches appears to be trivial for humans, so cannot be said for machines. This holds particularly true when artists use artistic cues such as shadows to represent depth. Additionally, hand drawn sketches are intrinsically imperfect and might contain curves making the gap between raster to vector hard to reduce. In this research, a method that automatically converts hand-drawn sketches in presence of shadows and curves is presented. The following study is divided into two section. The first section deals with junction localisation and identification to ensure that the topological fidelity of the drawing is retained. When compared to current state of the art, the results obtained shows an improvement of 61% when the proposed methodology was evaulated for junction spatial localisation using Salient Point Error over the same dataset. Even though junction type identification was not used during the proposed vectorisation pipeline, a number of methods were described and evaluated for junction type classification. Classification was performed using three methodologies and the best classification results obtained an F-score of 0.95. The second section dealt with contour extraction to remove shadows and other artefacts from the drawings. Each pixel was assigned an orientation based on the direction of the surrounding pixels and used the result was used to identify the path between two connected junctions. Unconnected lines and recovery of missed junctions were also considered. An average F-measure score of 0.992 was obtained over the whole dataset when the ground truths and the vectorised images were compared using a contour evaluation protocol. A dataset of 17 images was used to cover drawings created in sketching software and on paper using straight and curved lines. The reconstruction was performed using either lines, arcs or splines as deemed the most adequate. We showed that our method performed better in junction and contour detection and the results obtained were consistent throughout the whole dataset including straight and curved lines drawings.