Interactive Contour Extraction via Sketch-Alike Dense-Validation Optimization

Yongwei Nie, Xu Cao, Ping Li, Qing Zhang, Zhensong Zhang, Guiqing Li, Hanqiu Sun

Abstract: We propose an interactive contour extraction method inspired by a skill often adopted in sketching: an artist usually sketches an object by first drawing lots of short, directional, and redundant strokes, then following these small strokes to draw the final outline of the object. Our method simulates this process. To extract a contour, our method relies on user interaction, which provides us with a narrow band containing the target contour. Then, we densely sample sub-bands from the whole band, with each sub-band containing a local segment of the target contour. We design a curve-centered coordinate system in which a dynamic programming algorithm is proposed to extract the local segment in each sub-band. The local segment is guaranteed to be as evident and smooth as possible, to mimic the strokes sketched by the artist. Finally, we integrate all local segments of all sub-bands together to obtain the whole target contour based on the weighted principal component analysis. Our method can extract high-quality object contours due to the dense validations among local segments. That is, even if one segment deviates from the right location, several other segments in its local neighborhood can correct it in the integration stage. Both quantitative experiments and a user study demonstrate the effectiveness of the proposed method.