scholarly journals Point distribution form model for spruce stems (Picea abies [L.] Karst.)

2019 ◽  
Vol 48 (No. 4) ◽  
pp. 150-155 ◽  
Author(s):  
M. Křepela
Keyword(s):  
Shape Analysis ◽  
Principal Components ◽  
Distribution Model ◽  
Experimental Plot ◽  
Point Distribution ◽  
Distribution Form ◽  
Form Model ◽  
Components Analysis ◽  
Point Distribution Model ◽  
Stem Shape

The paper deals with the construction of a point distribution form model for spruce stems. This model is based on the principal components analysis of variance-covariance matrix formed for the Procrustes residuals. The calculation of full Procrustes co-ordinates, the principal components, is demonstrated on an example of a spruce experimental plot at premature age, and a point distribution model is constructed for the first three components. The parameters of the model are evaluated in relation to Konšel’s (Kraft’s) tree classes, normality of their classification is tested, maxima and minima are demonstrated on actual trees. The complete stem shape analysis of all four samples is also provided. A special model is constructed for these samples and the course of the parameters of this model is graphically represented.

scholarly journals A Multiscale Constraints Method Localization of 3D Facial Feature Points

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Hong-an Li ◽  
Yongxin Zhang ◽  
Zhanli Li ◽  
Huilin Li
Keyword(s):  
Facial Feature ◽  
Feature Point ◽  
Distribution Model ◽  
Feature Points ◽  
Widespread Application ◽  
Point Distribution ◽  
Localization Accuracy ◽  
Localization Method ◽  
Relative Angle ◽  
Point Distribution Model

It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative angle histogram of each vertex in a 3D point distribution model is calculated; then the cluster set of the facial feature points is determined using the cluster algorithm. Finally, the feature points are located precisely according to multiscale integral features. The experimental results show that the feature point localization accuracy of this algorithm is better than that of the localization method using the relative angle histograms.

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