基于自适应超像素分割的点刻式DPM区域定位算法研究

Stippled Direct Part Mark Location Based on Self-adaptive Super-pixels Segmentation

为解决点刻式直接零件标志(Direct part mark,DPM)码基本单元分割困难、区域定位欠精确等问题,提出使用超像素分割和谱聚类相结合的算法,对含有DPM区域的图像进行初步分割和精确定位.首先为提高超像素分割的准确、快速和完整性,本文利用近邻传播聚类思想实现自动聚类得到超像素区域,并引入边缘置信度调整超像素边缘,形成自适应边缘简单线性迭代聚类(Adaptive edge simple linear iterative clustering,AE-SLIC)算法.该算法改进了简单线性迭代聚类(Simple linear iterative clustering,SLIC)超像素分割算法存在的未明确界定超像素区域边缘信息和分割数目无法自适应确定等问题;其次,将超像素作为谱聚类中图的顶点进行二次聚类,DPM区域内超像素因相似度高而被聚集为一类,从而完成点刻式DPM区域的精确定位.经实验测试和分析,本文算法得到的超像素分割结果在完整性、运算复杂度等方面优于常见的超像素分割算法.与基于像素点运算的传统定位算法相比,本文算法具有良好的实时性、定位准确率和鲁棒性.

In order to solve the problem existing in segmentation and location of the stippled direct part mark （DPM） code, this paper combines the advantages of super-pixels segmentation and spectral clustering algorithm to pre-segment and precisely locate the DPM area. First, we propose an adaptive edge simple linear iterative clustering （AE-SLIC） super-pixels segmentation algorithm to achieve accurate, fast and integral segmentation. The super-pixels are generated by a？nity propagation automatically and edges of super-pixels are adjusted by edge confidence in AE-SLIC, which has improved the problems of unclear definition of edge and non-adaptive number of super-pixels in the simple linear iterative clustering （SLIC） algorithm. Second, the super-pixels are treated as the vertexes of spectral clustering. Then the location of stippled DPM code is completed by the clustered group of the super-pixels. The experimental results demonstrate the superior performance of the AE-SLIC algorithm in terms of segmentation accuracy and computation e？ciency. Through comparison with the traditional location algorithm based on the operation of pixels, the proposed algorithm shows its property of real-time, location accuracy and robustness to the noise disturbance.