An Improved Multi-Level Set C-V Model for Grading of Korean Pine Seeds

Abstract

An improved multi-level set C-V model for non-destructive grading of Korean pine seeds is presented in this paper. On the basis of improved Ostu and rough segmentation results of expansion operation, the improved C-V model is used to extract the target contour of Korean pine seeds; the characteristic parameters of fruit length and maximum transverse diameter are extracted by mathematical morphology method, and polynomial fitting is carried out with the actual measured values to construct a mathematical model with better quality; according to the extracted characteristic parameters, a comprehensive evaluation and grading standard for Korean pine seeds is established. The experimental results show that this method can achieve simultaneous classification of multiple Korean pine seeds, and the average accuracy of classification can be up to 97.2%.