基于NVST觀測的米粒識別和形態(tài)特征分析
發(fā)布時間：2018-07-16 08:40
【摘要】：米粒組織是太陽表層對流運動所產(chǎn)生的一種形態(tài)特征.由于米粒強度分布不均勻以及邊緣比較模糊,使得采用傳統(tǒng)的基于強度和梯度閾值的方法來準確地識別它們變得困難.因此,本文提出了一個基于相位一致性的米粒識別算法.選用中國科學院云南天文臺撫仙湖的新一代太陽真空望遠鏡(New Vaccum Sloar Telescope,簡稱NVST)的高分辨活動區(qū)觀測資料來展示算法的識別過程,并根據(jù)識別結(jié)果分析了米粒的形態(tài)特征.選取了2個目前已存在的米粒識別算法來驗證所提算法的準確性和有效性,實驗結(jié)果表明所提算法能夠有效地提取那些低對比度的米粒特征.同時為了檢驗算法對閾值的響應程度,分別選取了3組閾值來檢驗不同閾值情況下的差異性.結(jié)果證明所提算法對閾值響應不敏感.為了進一步檢驗所提算法獲取統(tǒng)計結(jié)果的準確性,對米粒的直徑、強度、形狀以及分形維數(shù)進行統(tǒng)計.統(tǒng)計結(jié)果很好地符合了已有文獻的結(jié)論,這也進一步驗證所提算法的準確性和健壯性,能夠用于進一步的科學研究.
[Abstract]:Rice grain tissue is a morphological feature produced by the convective motion of the surface layer of the sun. Because of the uneven distribution of grain strength and fuzzy edges, it is difficult to identify them accurately by using traditional methods based on intensity and gradient threshold. Therefore, this paper proposes a phase consistency based rice grain recognition algorithm. The high-resolution active region observation data of the New Generation of New Vaccum Sloar Telescope( NVST) at Fuxian Lake, Yunnan Observatory, Chinese Academy of Sciences, are used to demonstrate the recognition process of the algorithm. The morphological characteristics of rice grains are analyzed according to the recognition results. Two existing rice grain recognition algorithms are selected to verify the accuracy and validity of the proposed algorithm. The experimental results show that the proposed algorithm can extract the low-contrast rice grain features effectively. At the same time, in order to test the response of the algorithm to the threshold, three groups of thresholds were selected to test the differences under different threshold conditions. The results show that the proposed algorithm is insensitive to threshold response. In order to check the accuracy of the proposed algorithm, the diameter, intensity, shape and fractal dimension of the grain were analyzed. The statistical results are in good agreement with the existing conclusions, which further verify the accuracy and robustness of the proposed algorithm, and can be used for further scientific research.
【作者單位】： 昆明理工大學信息工程與自動化學院 云南省計算機技術應用重點實驗室;
【基金】：國家自然科學基金(U1231205, U1531132, 11463003, 11573012, 11303011)資助
【分類號】：P182.2
本文編號：2125858
[Abstract]:Rice grain tissue is a morphological feature produced by the convective motion of the surface layer of the sun. Because of the uneven distribution of grain strength and fuzzy edges, it is difficult to identify them accurately by using traditional methods based on intensity and gradient threshold. Therefore, this paper proposes a phase consistency based rice grain recognition algorithm. The high-resolution active region observation data of the New Generation of New Vaccum Sloar Telescope( NVST) at Fuxian Lake, Yunnan Observatory, Chinese Academy of Sciences, are used to demonstrate the recognition process of the algorithm. The morphological characteristics of rice grains are analyzed according to the recognition results. Two existing rice grain recognition algorithms are selected to verify the accuracy and validity of the proposed algorithm. The experimental results show that the proposed algorithm can extract the low-contrast rice grain features effectively. At the same time, in order to test the response of the algorithm to the threshold, three groups of thresholds were selected to test the differences under different threshold conditions. The results show that the proposed algorithm is insensitive to threshold response. In order to check the accuracy of the proposed algorithm, the diameter, intensity, shape and fractal dimension of the grain were analyzed. The statistical results are in good agreement with the existing conclusions, which further verify the accuracy and robustness of the proposed algorithm, and can be used for further scientific research.
【作者單位】： 昆明理工大學信息工程與自動化學院 云南省計算機技術應用重點實驗室;
【基金】：國家自然科學基金(U1231205, U1531132, 11463003, 11573012, 11303011)資助
【分類號】：P182.2
【相似文獻】
相關碩士學位論文 前1條
1 陳宇超;NVST多通道高分辨觀測系統(tǒng)軟件設計和實現(xiàn)[D];中國科學院研究生院(云南天文臺);2015年
，本文編號：2125858
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