基于数字全息成像的淡水藻类检测与分类技术研究

发布时间：2018-05-26 23:35

本文选题：藻类 + 全息成像　；参考：《南昌航空大学》2017年硕士论文

【摘要】：藻类是所有植物中最古老的,大多数藻类生活在水中。藻类不仅具有为水域渔业生产提供营养基础的重要意义,而且可以通过水体中藻类细胞的数量为判断水质是否污染提供依据。在对藻类识别技术的研究中,传统的研究步骤由为显微观测、形态分析以及计数统计组成。主要通过人眼观测,易导致视觉疲劳、效率低、且数据不能保存。随着现代数字图像处理技术的发展,实现了基于数字全息对藻类细胞进行识别计数与分析。数字全息与传统光学全息相比具有制作成本低,成像速度快,记录和再现灵活等优点,其记录与再现过程都可以通过数字化处理。对全息再现的藻类样本图进行HOG特征提取,结合SVM监督学习模型实现对藻类细胞高效、便捷的藻类细胞分类计数。本文首先对数字全息无透镜成像的基本理论进行分析。针对藻类细胞的特点,基于无透镜全息成像理论制作出简单易操作的无透镜全息成像装置。在此装置的基础上进行藻类的计数与分析。本论文具体内容如下:1.针对目前传统藻类检测的不足,本文采用数字图像处理技术对藻类细胞进行研究。2.介绍了无透镜全息成像原理以及展示了无透镜全息成像的光路图。对无透镜全息再现技术进行详细论述,根据无透镜全息理论与本论文对藻类细胞研究需要,设计了一个可以易于携带且质量轻、体积小的全息无透成像装置,并且利用3D打印机做出实物装置。介绍具体使用操作步骤以及对比了此装置与传统装置相比具有的优势。3.为了对藻类细胞进行快速、高效的特征提取,运用HOG描述算子对藻类进行研究。HOG仅在图像的局部单元上操作,因此它对图像几何与光学的形变都能保持较好的不变性。此外,只要在粗的空域抽样、精细的方向抽样以及较强的局部光学归一化等条件下,藻类细胞检测效果就不会受其他因素影响。HOG特征适合用于图像中的藻类检测。4.利用SVM监督学习模型对藻类细胞进行目标识别且进行计数与分析。SVM与传统学习方法(如模式识别、神经网络)相比,它基于结构风险最小化原则,泛华能力强。它是一个凸优化问题,因此局部最优解一定是全局最优点。此外,SVM解决了线性与非线性的分类问题。
[Abstract]:Algae are the oldest of all plants, and most of them live in water. Algae not only has the important meaning of providing nutritive basis for fishery production in water area, but also provides the basis for judging whether the water quality is polluted or not by the number of algae cells in the water body. In the research of algal recognition technology, the traditional research steps consist of microscopic observation, morphological analysis and counting statistics. Mainly through the human eye observation, easy to lead to visual fatigue, low efficiency, and data can not be preserved. With the development of modern digital image processing technology, algal cell recognition counting and analysis based on digital holography is realized. Compared with traditional optical holography, digital holography has the advantages of low cost, fast imaging speed, flexible recording and reproducing, and can be digitally processed. The holographic reconstruction of algae sample map was performed with HOG feature extraction and the SVM supervised learning model was used to realize the efficient and convenient classification and counting of algal cells. In this paper, the basic theory of digital holographic lensless imaging is analyzed. Based on the lensless holographic imaging theory, a simple and easy to operate lensless holographic imaging device was developed according to the characteristics of algae cells. On the basis of this device, algae counting and analysis are carried out. The content of this thesis is as follows: 1. Aiming at the deficiency of traditional algal detection at present, this paper uses digital image processing technology to study algal cells. 2. 2. The principle of lensless holographic imaging and the optical path of lensless holographic imaging are introduced. Based on the theory of lensless holography and the need of algae cell research in this paper, a holographic imaging device is designed, which is easy to carry, light in weight and small in volume. And using a 3D printer to make a physical device. The operation steps are introduced and the advantages of this device compared with the traditional device are compared. In order to extract the algal cells quickly and efficiently, the HOG description operator is used to study the algae. Hog only operates on the local unit of the image, so it can keep good invariance to the geometric and optical deformation of the image. In addition, under the conditions of coarse spatial sampling, fine direction sampling and strong local optical normalization, the effect of algal cell detection will not be affected by other factors. Compared with traditional learning methods (such as pattern recognition, neural network), SVM supervised learning model is based on structural risk minimization principle. It is a convex optimization problem, so the local optimal solution must be the global optimal. In addition, SVM solves the problem of linear and nonlinear classification.
【学位授予单位】：南昌航空大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：Q949.2;TP391.41

【参考文献】