联合骨架与边界特征的平面形状分解

在形状分析及其相关应用中,将形状分解成有意义的几个部分往往具有重要意义。骨架与轮廓都蕴含了丰富的物体形状全局与局部信息。提出一种联合骨架与边界特征的平面形状分解方法。该算法引入符合视觉特性的弯曲度比率作为约束,获得可控的分解结果以满足不同细节尺度的要求。算法充分利用对轮廓进行离散曲线演化时得到的信息,避免几个重要的部分被合并为一个整体。由于采用了鲁棒的骨架生成方法,使得算法对较高噪声干扰具有一定的鲁棒性,且能使得一个重要的部分不被进一步误分解。以MPEG7形状库等形状为实验对象,对算法的有效性进行了验证。分解实验结果均较为符合人类的主观感觉,同时对噪声污染的形状也具有较为鲁棒的结果。

Planar shape decomposition combining skeletal and boundary features

Shape decomposition usually plays a significant role in shape analysis and its various applications. In this paper, we present a shape decomposition algorithm that combines the strength of skeleton and boundary features, which carry both global and local information of object shapes. In the proposed method, a bending potential ratio is introduced as a constraint to generate controllable decomposition results. Besides, the algorithm is able to avoid conglutination of important parts by fully utilizing the discrete curve evolution information on the boundary. Furthermore, the adopted robust skeleton method ensures noise insensitive decomposition results and avoids decomposing important parts into trivial ones. We choose the MPEG7 shape dataset and other traditional testing shapes as our experiment data. Experimental results show that our method satisfies subjective visual perception on shape decomposition and is robust to large shape noise.