pattern intensity meaning in English
类型强度
Examples
- To array ccd , computing the interference pattern excursion based on image processing , fourier transform and interference pattern intensity distribution are discussed
对于面阵ccd ，提出了基于图像处理、傅里叶变换以及条纹亮度分布计算条纹漂移量三种方法。 - The binarization , dilation and thinning of interference pattern are stressed . computing the excursion of interference pattern based on fourier transform is based on the fact that interference pattern has different phase at different position , the signal extraction of interference pattern and phase unwrapping algorithm are mainly discussed . computing the excursion of the interference pattern based on the interference pattern intensity distribution is to position the fringe according to the intensity distribution characteristic
基于图像处理计算干涉条纹漂移量，目的是抽取干涉条纹的骨架，通过条纹骨架的定位实现对条纹的定位，重点讨论了条纹图像的二值化、膨胀和细化；基于傅里叶变换计算干涉条纹的漂移量，是基于条纹不同位置处的相位不同这一事实，重点讨论了干涉条纹信号的提取和干涉条纹相位去包裹算法；基于条纹亮度分布计算条纹漂移量，是根据条纹自身亮度分布的特点对条纹进行定位。 - Previous researchers have always determined the sp atial distribution patterns ( sdp ) of castanopsis kawakamii with a sample - dis tance method . however , the distribution patterns may be affected by the quadrat si ze and , in the course of analysis , the density differences among the cluster plots are not considered ; therefore , differences of cluster plot size and the dispersi on degree among individuals of cluster plots can not be known . authers of this pa per have determined the spatial distribution patterns of castanopsis kawakamii population in different habitats by means of non - quadrat distance method and a nalysed the pattern intensity and grain of the sdp . the pattern intensity is defi ned with the relative density differences and the pattern grain can embody the d ispersion degree of the individuals in the plots , and the dispersion degree among the plots . the determined results are as follows . the intensities of the species range in order from strong to week : litsea mollifolia p . kawakamii i . purpure a r . cochinchinensis c . kawakamii c . carlessii d . oldphamii s . superba . the gra ins of the species queue in order from coarse to close : s . superba = litsea mollif olia r . cohinchinensis c . kawakamii = i . purpurea c . carlessii p . racemosam d . oldp hamii . these determined results tally basiclly with the results authers of this paper have got in determining the same plots by means of aggregate index access ing method . in view of this , it is held that the sdp of c . kawakamii is closely related to the habitats and biological features
前人都是采用样方方法对格氏栲种群数量的空间格局进行测定，而格局分布有可能受样方大小的影响，且分析过程中没有涉及聚块间密度差的问题，因而无法掌握种群的聚块大小差别及聚块内个体间的离散程度.本研究采用无样方距离法，测定不同生境的格氏栲种群空间格局，分析格氏栲种群格局的强度和纹理.强度以聚块和间隙的密度差来定义，纹理则是体现聚块内个体间的离散程度与诸聚块间的分离程度.测定结果表明，格氏栲种群格局强度从高到低排列次序为：木姜子蚊母树冬青茜草树格氏栲米槠虎皮楠木荷；格局纹理从粗到细的顺序是：木荷=木姜子茜草树格氏栲=冬青米槠蚊母树虎皮楠.这一测定结果与作者采用聚集度指标测定相同样地格氏栲种群空间格局的结果基本相符.因此，格氏栲空间格局类型及分布与格氏栲生物学特性及生境的关系密切