基于數字全息成像的淡水藻類檢測與分類技術研究
本文選題:藻類 + 全息成像 ; 參考:《南昌航空大學》2017年碩士論文
【摘要】:藻類是所有植物中最古老的,大多數藻類生活在水中。藻類不僅具有為水域漁業(yè)生產提供營養(yǎng)基礎的重要意義,而且可以通過水體中藻類細胞的數量為判斷水質是否污染提供依據。在對藻類識別技術的研究中,傳統(tǒng)的研究步驟由為顯微觀測、形態(tài)分析以及計數統(tǒng)計組成。主要通過人眼觀測,易導致視覺疲勞、效率低、且數據不能保存。隨著現代數字圖像處理技術的發(fā)展,實現了基于數字全息對藻類細胞進行識別計數與分析。數字全息與傳統(tǒng)光學全息相比具有制作成本低,成像速度快,記錄和再現靈活等優(yōu)點,其記錄與再現過程都可以通過數字化處理。對全息再現的藻類樣本圖進行HOG特征提取,結合SVM監(jiān)督學習模型實現對藻類細胞高效、便捷的藻類細胞分類計數。本文首先對數字全息無透鏡成像的基本理論進行分析。針對藻類細胞的特點,基于無透鏡全息成像理論制作出簡單易操作的無透鏡全息成像裝置。在此裝置的基礎上進行藻類的計數與分析。本論文具體內容如下:1.針對目前傳統(tǒng)藻類檢測的不足,本文采用數字圖像處理技術對藻類細胞進行研究。2.介紹了無透鏡全息成像原理以及展示了無透鏡全息成像的光路圖。對無透鏡全息再現技術進行詳細論述,根據無透鏡全息理論與本論文對藻類細胞研究需要,設計了一個可以易于攜帶且質量輕、體積小的全息無透成像裝置,并且利用3D打印機做出實物裝置。介紹具體使用操作步驟以及對比了此裝置與傳統(tǒng)裝置相比具有的優(yōu)勢。3.為了對藻類細胞進行快速、高效的特征提取,運用HOG描述算子對藻類進行研究。HOG僅在圖像的局部單元上操作,因此它對圖像幾何與光學的形變都能保持較好的不變性。此外,只要在粗的空域抽樣、精細的方向抽樣以及較強的局部光學歸一化等條件下,藻類細胞檢測效果就不會受其他因素影響。HOG特征適合用于圖像中的藻類檢測。4.利用SVM監(jiān)督學習模型對藻類細胞進行目標識別且進行計數與分析。SVM與傳統(tǒng)學習方法(如模式識別、神經網絡)相比,它基于結構風險最小化原則,泛華能力強。它是一個凸優(yōu)化問題,因此局部最優(yōu)解一定是全局最優(yōu)點。此外,SVM解決了線性與非線性的分類問題。
[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.
【學位授予單位】:南昌航空大學
【學位級別】:碩士
【學位授予年份】:2017
【分類號】:Q949.2;TP391.41
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