相鄰檢波點 的英文怎麼說
中文拼音 [xiānglīnjiǎnbōdiǎn]
相鄰檢波點
英文
adjacent geophone station- 相 : 相Ⅰ名詞1 （相貌; 外貌） looks; appearance 2 （坐、立等的姿態） bearing; posture 3 [物理學] （相位...
- 檢 : Ⅰ動詞1 （查） check up; inspect; examine 2 （約束; 檢點） restrain oneself; be careful in one s c...
- 波 : Ⅰ名詞1 （波浪） wave 2 [物理學] （振動傳播的過程） wave 3 （意外變化） an unexpected turn of even...
- 點 : Ⅰ名詞1 （液體的小滴） drop （of liquid） 2 （細小的痕跡） spot; dot; speck 3 （漢字的筆畫「、」）...
- 相鄰 : adjoin; adjoining; adjacent
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And then the paper gives the image processing methods used in the geometrical parameter measuring system. according to the process of the image processing, it introduces the following three methods in order : image pretreatment method which consists of the average value of neighbour area and lowpass ; image segmentation method which involves threshold segmentation, edge detection, image thinning and edge connecting ; the image matching method consiting of picking up feature point, matching and rebuilding
然後給出了在幾何量測量系統中所用到的圖象處理方法。依照圖象處理的過程，先後介紹了圖象預處理的方法，包括鄰域平均法和低通濾波法；圖象分割方法，包括二值化、邊緣檢測、細化以及邊緣連接等；圖象匹配的相關方法，包括特徵點的提取、匹配及重建。In this dissertation, the research trends for the problem have been introduced ; the ‘ dim ’ and ‘ point ’ has been strictly defined in mathematics from machine vision and human vision ; the ideal clutter suppression system based on clutter predication and the realization and evaluation of evaluation index has been studied, in succession the clutter suppression technologies have been researched. firstly, the classic nonparametric algorithm has been analyzed in detail and systematically, for it ’ s weakness that it cannot remove the non - stationary clutter ideally, kalman filter algorithm for clutter suppression in 2d image signal has been built. secondly, fast adaptive kalman filter is presented based on fast wide - sense stationary areas partition algorithm : limited combination and division algorithm based on quarti - tree algorithm, new taxis filter route algorithm which can break through the limitation of the necessity of pixel neighborhood of 2d filter and laplace data model with two parameters which is perfectly suitable for the residual image of kalman clutter suppression
首先分析了經典的非參數法，對於四種具有代表性的核，從前述的三個性能評價方面做了分析和對比，指出了其速度快的優點和對非平穩圖像適應性差的弱點，針對非參數法的弱點，重點研究了對非平穩圖像適應良好的卡爾曼雜波抑制技術:建立了非平穩圖像的類自回歸模型，在此基礎上建立了二維卡爾曼濾波基礎的兩個方程:狀態方程和測量方程;建立了非平穩圖像準平穩區域快速劃分演算法:基於四叉樹法的有限分裂合併演算法;二維空間的基於k排序的濾波路線演算法，突破了空域濾波路線上區域相鄰的限制;在這些研究的基礎上實現了快速卡爾曼估計，實驗驗證了該方法相對逐點卡爾曼估計可以提高運算速度三倍左右;雜波抑制結果表明傳統的高斯性檢驗並不適合卡爾曼估計后的殘余圖像，由此建立了殘余圖像的雙參數拉普拉斯模型，實驗表明其可以完好的吻合殘余圖像的概率密度曲線。
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