Style enhanced line drawings based on multi-feature

Shiguang Liu, Ziqi Liu

Abstract: Line drawing is a means of superior visual communication, which is made up of lines. Artists usually show their unique styles while creating a line drawing. However, traditional methods can not effectively simulate this free-hand style of artists. Though the data-driven method can generate abundant styles, it is complex and time-consuming. To this end, a new style enhanced line drawing method was proposed. First, lines in images were extracted based on edge detection and edge tracking methods. Then, the global drawing features were simulated, including length measurement, overlap measurement and offset measurement. Finally, the local drawing features that contain width measurement, sharpness measurement and depth measurement were simulated. The results showed that our method can generate stylized line drawings that are more similar to free-hand drawings of artists than the state-of-the-art methods.