EZ-Sketching:
Three-Level Optimization for Error-Tolerant Image Tracing

Patent Pending

ACM Transaction on Graphics (Proceedings of ACM SIGGRAPH 2014)

  Qingkun Su1    Wing Ho Andy Li2    Jue Wang3    Hongbo Fu2

1HKUST        2City University of Hong Kong        3Adobe Research

 

EZ-Sketching

The proposed system automatically refines sketch lines (a, c, e) (created by different users) roughly traced over a single image in a three-level optimization framework. The refined sketches (b, d, f) show closer resemblance to the traced images and are often aesthetically more pleasing, as confirmed by the user study.

Abstract
We present a new image-guided drawing interface called EZ-Sketching, which uses a tracing paradigm and automatically corrects sketch lines roughly traced over an image by analyzing and utilizing the image features being traced. While previous edge snapping methods aim at optimizing individual strokes, we show that a co-analysis of multiple roughly placed nearby strokes better captures the user's intent. We formulate automatic sketch improvement as a three-level optimization problem and present an efficient solution to it. EZ-Sketching can tolerate errors from various sources such as indirect control and inherently inaccurate input, and works well for sketching on touch devices with small screens using fingers. Our user study confirms that the drawings our approach helped generate show closer resemblance to the traced images, and are often aesthetically more pleasing.
Paper

Slides
Slides for presentation at SIGGRAPH 2014
Video
BibTeX
@ARTICLE{EZSketching:2014,
author = {Qingkun Su and Wing Ho Andy Li and Jue Wang and Hongbo Fu},
title = {EZ-Sketching: Three-Level Optimization for Error-Tolerant Image Tracing},
journal = {ACM Trans. Graph (Proceedings of SIGGRAPH 2014)},
year = {2014},
volume = {33},
pages = {4}
}
Thanks

We thank the reviewers for their constructive comments, the user study participants for their time and the Flickr users for making their images available through creative common licenses. This work was substantially supported by grants from the RGC of HKSAR (CityU 113513) and The City University of Hong Kong (7002925).