An Accurate System for Fashion Hand-Drawn Sketches Vectorization

Luca Donati, Simone Cesano, Andrea Prati
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
a) Input sketch (b) AI TM Live Trace (c) Simo-Serra et al. [12] (d) Our method Figure 1: Visual comparison of three line extraction algorithms tested over a portion of a hand-drawn sketch (a). Our proposed method (d) grants optimal "recall" performance compared to state of the art (c)), without bargaining its "precision". Traditional approaches like Adobe Illustrator TM Live Trace (b) fail greatly in both "precision" and "recall" performance, and need to be manually fine tuned. Abstract
more » ... d. Abstract Automatic vectorization of fashion hand-drawn sketches is a crucial task performed by fashion industries to speed up their workflows. Performing vectorization on hand-drawn sketches is not an easy task, and it requires a first crucial step that consists in extracting precise and thin lines from sketches that are potentially very diverse (depending on the tool used and on the designer capabilities and preferences). This paper proposes a system for automatic vectorization of fashion hand-drawn sketches based on Pearson's Correlation Coefficient with multiple Gaussian kernels in order to enhance and extract curvilinear structures in a sketch. The use of correlation grants invariance to image contrast and lighting, making the extracted lines more reliable for vectorization. Moreover, the proposed algorithm has been designed to equally extract both thin and wide lines with changing stroke hardness, which are common in fashion hand-drawn sketches. It also works for crossing lines, adjacent parallel lines and needs very few parameters (if any) to run. The efficacy of the proposal has been demonstrated on both hand-drawn sketches and images with added artificial noise, showing in both cases excellent performance w.r.t. the state of the art.
doi:10.1109/iccvw.2017.268 dblp:conf/iccvw/DonatiCP17 fatcat:x4rg4skxcjcjhnrazwtmmuro6m