| 1. | Sample central moments
样本中心矩 |
| 2. | Second central moment
二阶中心矩 |
| 3. | Third central moment
三阶中心矩 |
| 4. | The influences of maximum diameter of rainfall particles on the central moment method of the third power of velocity
降水粒子的最大直径对速度三阶中心矩法的影响 |
| 5. | Finally , getting the centroid by calculating the central moment of image , the centroid is obtaind and the results are showed on the screem
通过计算图像的中心矩而得到的图像中二维目标质心,并在界面上显示质心计算结果。 |
| 6. | Two methods are presented , one is based on the normalized central moments of one dimensional range profiles , the other is based on one dimensional scattering centers matching
提出了基于目标一维平均距离像归一化中心矩的目标识别方法和一种新的基于目标一维散射中心匹配的目标识别方法。 |
| 7. | These moments include normalized central moments , hu ' s moment invariants , affine moment invariants , and tsirikolias - mertzios moments . the classification techniques used here are euclidean distance measure , normalized cross correlation and discrimination cost
这些矩包括规格化后的中心矩、 hu矩不变量、仿射矩不变量和tsirikolias -的北丁业人学6卜学位论义搁要mertzios矩。 |
| 8. | The correctness is over 99 % . ( 5 ) shape features studied were aspect , first invariant central moment , elongatedness , roundness , circularity and thickness . aspect and first invariant central moment are the most effective shape features for identifying monocotyledonous weed from dicotyledonous weed , and the correctness was 93 %
( 4 )利用修正的色度公式，由判别分析法确定色度阈值，对杂草图像进行阈值分割，能够有效地识别植物与非植物背景，正确识别率在99以上，但色度的计算量大于过绿特征的计算量，不利于杂草识别速度的提高。 |
| 9. | Wide applicability features in pearson distribution . based on the study of the distribution ' s central moments and origin moments , optimizing fitness method is given and by which the parameters characterizing the distribution are obtained . the application of pearson distribution in reliability evaluation obtains satisfied result
在对各型皮尔逊分布的中心矩与原点矩的研究基础之上，介绍了针对该分布的优化拟合方法，得到了各分布类型的参数并实现了其在工序能力评价中的应用。 |
