Research on Curing Tobacco Image Segmentation Based on K-means Clustering Algorithm
At present,the tobacco baking process in China mainly relies on manual monitoring,which has problems of subjectivity,fuzziness and high cost.Research on using machine vision methods to monitor and judge real-time changes in tobacco quality during the baking process is gradually increasing.Real time monitoring needs to be based on efficient and accurate segmentation of roasted tobacco leaf images,so the re-search on segmentation of roasted tobacco leaf images has become particularly important.A segmentation method for roasted tobacco leaf images based on K-means clustering algorithm was proposed.Firstly,the image was read and RGB was converted to the CYMK color space.Then,the grayscale image of the K-channel in the CYMK color space was extracted.We clustered the single channel image again,determined the image segmentation threshold based on the cluster center,and finally used image processing methods to segment the image.We compared three cluste-ring methods of K-means,fuzzy C-means clustering (FCM) and Gaussian mixture clustering (GMM).The results showed that the pixel accura-cy of the K-means algorithm was 97.8％,the intersection to union ratio was 96.43％,and the Dice coefficient was 98.2％,all of which were better than the other two methods.The K-means algorithm could better extract the contour of tobacco leaves,remove redundant information and make the segmentation results clearer.
Tobacco curingImage segmentationK-meansThreshold value
万方数据
维普
