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 应用科技  2019, Vol. 46 Issue (2): 65-70  DOI: 10.11991/yykj.201807005 0

### 引用本文

LI Ang, WANG Qiusheng, ZHANG Bei. Study on identification method of surface plasmon resonance absorb spectrum in back focal plane[J]. Applied Science and Technology, 2019, 46(2), 65-70. DOI: 10.11991/yykj.201807005.

### 文章历史

1. 北京航空航天大学 自动化科学与电气工程学院，北京 100191;
2. 北京航空航天大学 中法工程师学院，北京 100191

Study on identification method of surface plasmon resonance absorb spectrum in back focal plane
LI Ang1,2, WANG Qiusheng1, ZHANG Bei1
1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;
2. Sino-French Engineer School, Beihang University, Beijing 100191, China
Abstract: Surface plasmon resonance (SPR) technology is a detection method with high precision and high real-time characteristics. A surface plasmon resonance-based detection system detects material properties by identifying an absorption spectrum in a surface plasmon resonance back focal plane image. The absorption spectrum identification method is realized mainly by manual drawing or one-dimensional gray scale statistics. These two methods can’t effectively identify large-scale back focal plane images under strong noise background. To solve this problem, this paper proposes a high-efficiency absorption spectrum identification method based on Hough transform, morphology and least squares method, which has good performance in the image recognition problem of focal plane after plasmon resonance with strong noise. This method makes up for the shortcomings of existing methods and provides a new idea for the absorption spectrum identification of surface plasmon resonance.
Keywords: surface plasmon resonance    back focal plane    absorb spectrum    identification    Hough transform    morphology    least square method

1 后焦面吸收谱识别方法研究

1.1 基于霍夫变换的图像裁剪

1.1.1 基于全局与局部阈值相结合的二值化

 $T{\rm{ = }}a{T_g} + b{T_l}$ (1)

1.1.2 基于霍夫变换的通光孔径粗定位

1.1.3 粗定位结果的后焦面图像裁剪

1.2 通光孔径精确识别

1.3 基于径向灰度分布的吸收谱半径估计

1.3.1 吸收谱半径粗略估计

1.3.2 后焦面图像吸收谱提取

1.4 基于最小二乘法的吸收谱半径与中心识别

 $\left[ {\begin{array}{*{20}{l}} {2{x_1}}&{2{y_1}}&1 \\ {2{x_2}}&{2{y_2}}&1 \\ \vdots & \cdots & \vdots \\ {2{x_n}}&{2{y_n}}&1 \end{array}} \right]\left[ {\begin{array}{*{20}{c}} {{c_1}} \\ {{c_2}} \\ {{c_3}} \end{array}} \right] = \left[ {\begin{array}{*{20}{c}} {x_1^2 + y_1^2} \\ {x_2^2 + y_2^2} \\ \vdots \\ {x_n^2 + y_n^2} \end{array}} \right]$ (2)

 ${{X}} = ({{{A}}^{\rm{T}}}{{A}}){{{X}}^{\rm{T}}}{{b}}$

2 多幅测试图像的识别效果