W. Liu and J. Lladós (Eds.): GREC 2005, LNCS 3926, pp. 334 – 345, 2006. © Springer-Verlag Berlin Heidelberg 2006 Sketch Parameterization Using Curve Approximation Zhengxing Sun, Wei Wang, Lisha Zhang, and Jing Liu State Key Lab for Novel Software Technology, Nanjing University, P. R. China, 210093 szx@nju.edu.cn Abstract. This paper presents a method of parameterization for online freehand drawing objects based on a piecewise cubic Bezier curve approximation. The target is to represent sketches in a compact format within a certain error tolerance with lower computation to be practically adaptable for the online graphics input. A set of user’s intended breakpoints in digital ink is firstly produced in terms of pen speed and local curvatures. Each of strokes of a skechy shape is then parameterized by the optimization of piecewise Bezier curve approximation to minimize the fitting error between stroke path and the curve. The experimental results show both effective and efficient for a wide range of drawing graphic objects. 1 Introduction As computers become integrated into everyday life, pen-based user interface is considered as a primary input method. Moreover, the feature to rapidly visualize and deliver human’s ideas using graphic objects, which cannot be efficiently represented by speech or text, is highly desirable in graphic computing [1]. The rapid growth of graphic data has sustained the need for more efficient ways to represent and compress the sketchy graphic data. The data representing freehand sketching needs not only to be compressed in order to reduce the internal handling size and to transfer in low bandwidth, but also to preserve the original intention of user and the convenience of easy access of the information and for further processing such as shape recognition, cooperative design, idea permutation and so on. For existing techniques, the pen movements are typically captured by a digitizing tablet and stored as sampled pen points of their paths, so called as digital ink, while an image for receptor is captured. The drawback of this technique is that sketches transferred in image usually require considerable storage capacity and cannot be modified by receptor. Although there have been a large amount of experiments on sketchy graphics recognition, such as feature-based [2][3], graph-based [4][5], machine learning [6][7][8] and Parametric methods such as polygon [9], B-spline [10] and Bezier curve [11], most of them guess and convert the drawing sketches into regular shapes. However, they are charged with desertion of users’ intension, and are the burden of computation especially for mobile devices. Parametric methods fitting techniques have been considered in shape representation and classification. A benefit of these approaches is that they can approximate the path of pen movements during user drawing with a few parameters and they are computationally efficient. Only a few researches have bent themselves to this issue [11][12].