基于自适应超像素分割的点刻式DPM区域定位算法研究

王娟; 王萍; 王港

doi:10.16383/j.aas.2015.c140233

基于自适应超像素分割的点刻式DPM区域定位算法研究

doi: 10.16383/j.aas.2015.c140233

1.
天津大学电气与自动化工程学院天津 300072

基金项目:

河北省科技支撑项目 (12213519D)资助

详细信息

作者简介:
王娟 2015 年获得天津大学博士学位. 主要研究方向为模式识别与智能系统. E-mail: wangjuan85@tju.edu.cn

通讯作者:
王萍天津大学电气与自动化工程学院教授. 主要研究方向为模式识别方法及应用, 图像理解, 运动对象跟踪. E-mail: wangps@tju.edu.cn

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出版历程
- 收稿日期: 2014-04-08
- 修回日期: 2014-12-23
- 刊出日期: 2015-05-20

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

1.
College of Electrical and Automation Engineering, Tianjin University, Tianjin 300072

Funds:

Supported by Science and Technology Support Program of Hebei Province (12213519D)

摘要

摘要: 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.
- 超像素 /
- 自适应边缘简单线性迭代聚类算法 /
- 谱聚类 /
- 精确定位
Abstract: 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 affinity 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 efficiency. 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.
- Super-pixels /
- adaptive edge simple linear iterative clustering (AE-SLIC) algorithm /
- spectral clustering /
- accurate location

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参考文献(26)

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基于自适应超像素分割的点刻式DPM区域定位算法研究

doi: 10.16383/j.aas.2015.c140233

作者简介:
王娟 2015 年获得天津大学博士学位. 主要研究方向为模式识别与智能系统. E-mail: wangjuan85@tju.edu.cn

通讯作者:
王萍天津大学电气与自动化工程学院教授. 主要研究方向为模式识别方法及应用, 图像理解, 运动对象跟踪. E-mail: wangps@tju.edu.cn

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

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留言板

基于自适应超像素分割的点刻式DPM区域定位算法研究

doi: 10.16383/j.aas.2015.c140233

作者简介: 王娟 2015 年获得天津大学博士学位. 主要研究方向为模式识别与智能系统. E-mail: wangjuan85@tju.edu.cn

通讯作者: 王萍 天津大学电气与自动化工程学院教授. 主要研究方向为模式识别方法及应用, 图像理解, 运动对象跟踪. E-mail: wangps@tju.edu.cn

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出版历程

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

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出版历程

目录

作者简介:
王娟 2015 年获得天津大学博士学位. 主要研究方向为模式识别与智能系统. E-mail: wangjuan85@tju.edu.cn

通讯作者:
王萍天津大学电气与自动化工程学院教授. 主要研究方向为模式识别方法及应用, 图像理解, 运动对象跟踪. E-mail: wangps@tju.edu.cn