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一种基于先验标记特征的精准图像配准算法

刘天弼 冯瑞

刘天弼, 冯瑞. 一种基于先验标记特征的精准图像配准算法[J]. 华东师范大学学报(自然科学版). doi: 10.3969/j.issn.1000-5641.202021006
引用本文: 刘天弼, 冯瑞. 一种基于先验标记特征的精准图像配准算法[J]. 华东师范大学学报(自然科学版). doi: 10.3969/j.issn.1000-5641.202021006
LIU Tianbi, FENG Rui. An algorithm for precise image registration based on priori mark features[J]. Journal of East China Normal University (Natural Sciences). doi: 10.3969/j.issn.1000-5641.202021006
Citation: LIU Tianbi, FENG Rui. An algorithm for precise image registration based on priori mark features[J]. Journal of East China Normal University (Natural Sciences). doi: 10.3969/j.issn.1000-5641.202021006

一种基于先验标记特征的精准图像配准算法

doi: 10.3969/j.issn.1000-5641.202021006
基金项目: 国家重点研发计划(2017YFC0803702)
详细信息
    作者简介:

    刘天弼, 男, 博士研究生, 研究方向为计算机视觉及人工智能. E-mail: allenlew@163.com

    通讯作者:

    冯 瑞, 男, 教授, 博士生导师, 研究方向为计算机视觉、多媒体及模式识别. E-mail: fengrui@fudan.edu.cn

  • 中图分类号: TP391.7

An algorithm for precise image registration based on priori mark features

  • 摘要: 基因测序仪在读取基因序列之前需要将镜头与基因芯片精准对齐, 因此需要一种算法能够精确计算当前视场(Field of View, FOV)与理想位置的偏差. 预先在基因芯片上特定位置设置标记, 通过拍摄的图像分析当前镜头与基因芯片的位置误差: 首先通过提取图像灰度特征捕捉标记位置以初步对齐视场中心位置; 再捕捉标记上的多个关键点的坐标, 通过对关键点的坐标映射关系进行拟合, 就可得到精确的坐标和角度偏差. 实践和实验分析表明, 使用设计的图像配准算法能够得到对视场与基因芯片间的位置偏差计算的高精度结果.
  • 图  1  3种track标记设计示意图

    Fig.  1  Schematic diagram of three track mark designs

    图  2  于竖直方向截取图像做水平卷积示意图

    Fig.  2  Schematic diagram of the image in the vertical direction for horizontal convolution

    图  3  卷积运算捕捉cross标记示意图

    Fig.  3  Schematic diagram of the convolution operation to capture cross marks

    图  4  单个像素错位映射

    Fig.  4  Mapping of single pixel misalignment

    图  5  卷积核长度与容错角度的关系

    Fig.  5  Relationship between the length of the convolution kernel and the angle of fault tolerance

    图  6  不同的卷积核切面

    Fig.  6  Cross sections of various convolution kernels

    图  7  通过卷积捕捉track标记

    Fig.  7  Capturing a track mark by convolution

    图  8  不同卷积核在理想状态下的区分度

    Fig.  8  Differentiation of various convolution kernels under ideal conditions

    图  9  不同卷积核在噪声环境下的区分度

    Fig.  9  Differentiation of various convolution kernels in a noisy environment

    图  10  不同卷积核在串扰环境下的区分度

    Fig.  10  Differentiation of various convolution kernels in a light crosstalk environment

    图  11  5种抗串扰卷积核切面

    Fig.  11  Cross sections of five anti-crosstalk convolution kernels

    图  12  5种抗串扰效果

    Fig.  12  Anti-crosstalk effects of five convolution kernels

    表  1  位置与角度配准误差

    Tab.  1  Position and angle deviation in registration

    型号/分辨率cell大小/像素亮度串扰/%track宽度/像素track数量位置误差/像素角度误差/(°)
    2 560 × 2 1609 × 91598 × 80.243 10.010 882
    6 × 61068 × 80.274 60.012 292
    5 012 × 5 0129 × 92099 × 90.284 90.0063 76
    6 × 61569 × 90.330 50.007 397
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-28
  • 网络出版日期:  2020-06-12

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