Quantitative evaluation of flower colour pattern by image analysis and principal component analysis of Primula sieboldii E. Morren

Euphytica ◽  
2004 ◽  
Vol 139 (3) ◽  
pp. 179-186 ◽  
Author(s):  
Y. Yoshioka ◽  
H. Iwata ◽  
R. Ohsawa ◽  
S. Ninomiya
2020 ◽  
Vol 18 (3) ◽  
pp. 149-158
Author(s):  
Bixuan Cheng ◽  
Chao Yu ◽  
Heling Fu ◽  
Lijun Zhou ◽  
Le Luo ◽  
...  

AbstractRosa x odorata (sect. Chinenses, Rosaceae) is an important species distributed only in Yunnan Province, China. There is an abundance of wild variation within the species. Using 22 germplasm resources collected from the wild, as well as R. chinensis var. spontanea, R. chinensis ‘Old Blush’ and R. lucidissima, this study involved morphological variation analysis, inter-trait correlation analysis, principal component analysis and clustering analysis based on 16 morphological traits. This study identified a high degree of morphological diversity in R. x odorata germplasm resources and the variation coefficients had a distribution range from 18.00 to 184.04%. The flower colour had the highest degree of variation, while leaflet length/width had the lowest degree of variation. Inter-trait correlation analysis revealed that there was an extremely significant positive correlation between leaflet length and leaflet width. There was also a significant positive correlation between the number of petals and duration of blooming, and the L* and a* values of flower colour were significantly negatively correlated. Principal component analysis screened five principal components with the highest cumulative contribution rate (81.679%) to population variance. Among the 16 morphological traits, style length, sepal width, flower diameter, flower colour, leaflet length and leaflet width were important indices that influenced the morphology of R. x odorata. This study offers guidance for the further development and utilization of R. x odorata germplasm resources.


2015 ◽  
Vol 15 (01) ◽  
pp. 1550006 ◽  
Author(s):  
Tiene A. Filisbino ◽  
Gilson A. Giraldi ◽  
Carlos E. Thomaz

In the area of multi-dimensional image databases modeling, the multilinear principal component analysis (MPCA) and concurrent subspace analysis (CSA) approaches were independently proposed and applied for mining image databases. The former follows the classical principal component analysis (PCA) paradigm that centers the sample data before subspace learning. The CSA, on the other hand, performs the learning procedure using the raw data. Besides, the corresponding tensor components have been ranked in order to identify the principal tensor subspaces for separating sample groups for face image analysis and gait recognition. In this paper, we first demonstrate that if CSA receives centered input samples and we consider full projection matrices then the obtained solution is equal to the one generated by MPCA. Then, we consider the general problem of ranking tensor components. We examine the theoretical aspects of typical solutions in this field: (a) Estimating the covariance structure of the database; (b) Computing discriminant weights through separating hyperplanes; (c) Application of Fisher criterium. We discuss these solutions for tensor subspaces learned using centered data (MPCA) and raw data (CSA). In the experimental results we focus on tensor principal components selected by the mentioned techniques for face image analysis considering gender classification as well as reconstruction problems.


2014 ◽  
Vol 707 ◽  
pp. 352-355
Author(s):  
Hao Li

The principles-related knowledge of boiler is mainly applied to analyze the relationships between boiler efficiency and excess air coefficient and the main parameters, and also the method of optimizing operation of the boiler is proposed.Key words: image analysis method; principal component analysis; boiler efficiency; optimizing operation


2012 ◽  
Vol 32 (7) ◽  
pp. 0711001
Author(s):  
杨秀坤 Yang Xiukun ◽  
钟明亮 Zhong Mingliang ◽  
景晓军 Jing Xiaojun ◽  
张尚迪 Zhang Shangdi

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