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玉兰属35种植物分类特征性状排序的研究 被引量:4
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作者 赵东欣 傅大立 赵天榜 《安徽农业科学》 CAS 北大核心 2007年第36期11811-11813,共3页
首次采用形态分类特征性状排序方法对玉兰属Yulania Spach35种植物进行了研究。结果表明,该法可作为该属植物物种鉴定和区别的有效方法之一,并将35种植物一一加以区别。同时,还可作为该属新分类群和属下分类系统建立的科学依据。如35种... 首次采用形态分类特征性状排序方法对玉兰属Yulania Spach35种植物进行了研究。结果表明,该法可作为该属植物物种鉴定和区别的有效方法之一,并将35种植物一一加以区别。同时,还可作为该属新分类群和属下分类系统建立的科学依据。如35种植物形态分类特征性状排序结果分为4类,其第1类至第3类,恰与玉兰属植物形态分类的玉兰组Sect.Yulania、渐尖玉兰组Sect.Tuplipastrum和朱砂玉兰组Sect.Zhushayulania相吻合;第4类,仅朝阳玉兰Y.zhaoyangyulan1个新种。此外,还纠正了一些错误。 展开更多
关键词 玉兰属 35种植物 分类特征性状 新方法 分类 分类系统
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Morphological Study of Ficus deltoidea Jack in Malaysia
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作者 Nor Asiah Awang Sayed M.Zain Hasan Mohammad Shafie B.Shafie 《Journal of Agricultural Science and Technology(B)》 2013年第2期144-150,共7页
Ficus deltoidea Jack (Moraceae) or Mas Cotek is a small shrub with a great morphological variation. Measurement of 40 morphological traits had been done on 50 accessions to find the most significant characters that ... Ficus deltoidea Jack (Moraceae) or Mas Cotek is a small shrub with a great morphological variation. Measurement of 40 morphological traits had been done on 50 accessions to find the most significant characters that enable differentiation being done according to its variety groups. The data were analyzed with principal component analysis (PCA) and cluster analysis (CA) using cluster software package programme to produce the scatter diagram and dendrogram relationship of the taxa. The results showed that there were 25 morphological characters having the value of factor analysis greater than 0.60 from its principal component (PC) with the Eigen value greater than 1.0. 16 out of 40 morphological characters had been identified as having high values of correlation coefficient ranging from -0.783 to 0.890. The analysis showed that the most responsible characters in grouping the samples into different groups are the shape and size of leaf, number and color of dots on the leaf surface and characters of syconium. The scatter diagram of the accessions on the PC1 against PC2 showed six major groups. The dendrogram displayed the relationship among the accessions and within the dissimilarity distance = 19, it classified the samples into five major groups. Observation on F. deltoidea resulted in the findings of high variability among the accessions. The most significant characters in grouping accessions are the shapes of leaf base (BL), shape of leaf apex (SA), ratio of lamina width to lamina length (R), dots color at the lower midrib (DLM), color of young syconium (CYS), color of mature syconium (CMS) and the number of syconium on trees (DST). This study provides basic information for introduction of some particular traits and effective conservation of the species breeding programme. The morphological traits were found to be useful for the diversity studies and in identifying the variation. The actual figures of F. deltoidea obtained through this study enable comparison being done to the previous and in future study. Hence, the varieties that are extinct could be recognised. 展开更多
关键词 Ficus deltoidea cluster analysis DIVERSITY morphological variability principal component analysis
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Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier 被引量:10
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作者 王慧利 朱明 +1 位作者 蔺春波 陈典兵 《Optoelectronics Letters》 EI 2017年第2期151-155,共5页
In this paper, firstly, target candidate regions are extracted by combining maximum symmetric surround saliency detection algorithm with a cellular automata dynamic evolution model. Secondly, an eigenvector independen... In this paper, firstly, target candidate regions are extracted by combining maximum symmetric surround saliency detection algorithm with a cellular automata dynamic evolution model. Secondly, an eigenvector independent of the ship target size is constructed by combining the shape feature with ship histogram of oriented gradient(S-HOG) feature, and the target can be recognized by Ada Boost classifier. As demonstrated in our experiments, the proposed method with the detection accuracy of over 96% outperforms the state-of-the-art method. efficiency switch and modulation. 展开更多
关键词 classifier AdaBoost histogram automata symmetric pixel candidate similarity surround segmentation
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