改进的软K段主曲线算法及其在指纹骨架提取中的应用

Improved Soft K-Segments Algorithm for Principal Curves and Its Applications on Fingerprint Skeletonization Extraction

主曲线是一种基于非线性变换的特征提取方法,它是通过数据分布"中间"并满足"自相合"的光滑曲线,能较好抽取出数据的结构特征。针对软K段主曲线算法提取的指纹图像的骨架结构光滑度较差,而且提取的指纹图像骨架经常出现小圈和短枝的现象,本文在对软K段主曲线算法和指纹图像数据特点分析的基础上,引入了一个新的评判函数,并提出了改进的软K段主曲线算法,将该算法应用在提取指纹图像骨架上。实验结果表明,改进的软K段主曲线算法在提取指纹图像骨架的效果和准确率上比原算法都有明显提高。

Principal curves are a feature extraction method based on the nonlinear transformation.Meanwhile,they are smooth self-consistent curves that pass through the″middle″of the distribution and satisfy the″self coincidence″.Thus,structural features of the data can be extracted.Based on the soft K-segments algorithm for principal curves,the skeletonization extraction of the fingerprint image is not smooth enough,which often appears small circle and short branches.To solve this proplem,the soft K-segments algorithm for principal curves and the specialties of fingerprint are analyzed.A new evaluation function is also proposed.And an improved soft K-segments algorithm for principal curves is put forward.Compared with those of the original algorithms,the smoothness and the accuracy of the proposed algorithm can be illustrated by experiments.