CAJ | 학술논문

叶片的形态特征多样，它的叶形特征对植物的研究具有重要的意义。为了判断不同叶形之间的轮廓相似性，利用轮廓特征提取，将叶子的叶形存储在一维序列中。结合统计学，引用Pearson相关性进行特征分布的相关系数计算。实验结果显示，不同叶形的相关系数之间有较为显著的差异，测试的叶子叶形判别准确率较高，可作为判断叶形相似性的重要依据。
협편적형태특정다양，타적협형특정대식물적연구구유중요적의의。위료판단불동협형지간적륜곽상사성，이용륜곽특정제취，장협자적협형존저재일유서렬중。결합통계학，인용Pearson상관성진행특정분포적상관계수계산。실험결과현시，불동협형적상관계수지간유교위현저적차이，측시적협자협형판별준학솔교고，가작위판단협형상사성적중요의거。
Morphological characteristics of leaves could be diversified, and the leaf shape is significant to botanical research. In this research, contour characteristic extraction is used to store leaf shape into one-dimensional sequence so that the simi-larity between different contours of leaf shape can be estimated. Combined with statistics, Pearson product-moment is ad-opted to calculate correlation coefficient of two characteristic distributions. It’s suggested that the difference of correlation coefficients among different leaf shapes are significant, and the leaf shape recognition test is with high accuracy. This method can be applied as an essential measure to estimate leaf shape similarity.