Research On Tree Image Segmentation Method Based On LBF Algorithm

Tree image segmentation is an important link in the research of tree visual reconstruction and accurate target application of trees,which is of great significance for the evaluation of tree growth situation,tree volume rate and estimation of forest stock.In this paper,the classical image segmentation method,c-v model,LBF model and their improved models are studied theoretically,and different model algorithms are applied to conduct segmentation experiments on tree images.The specific contents are as follows:Studied the threshold segmentation method(iterative method,the OTSU method and local threshold value method),edge detection operator,Canny operator,Sobel operator,Roberts operator,Prewitt operator,Laplacian operator,LoG-Laplacian operator),and regional segmentation region growing method,regional division combined method(seed)of three kinds of algorithm theory,and apply these methods to trees image image segmentation experiments,and analyzes the experimental results.In this paper,the classical c-v model theory is studied,and the segmentation experiment is carried out with the tree image as the target.This paper studies the theory of c-v model,and based on the c-v model,carries out image segmentation experiments on tree images,and analyzes the segmentation results.This paper studied the traditional LBF(the Local Binary Fitting)model theory,and the weak edge segmentation effect is not ideal defect was improved,the application of the traditional LBF model and its improved algorithm of tree image segmentation experiments,studied the model parameters on the result of image segmentation based on improved LBF algorithm,and further obtain the optimal parameter set through the experiment,using the group parameter more groups of experiments,and compared with the traditional LBF model algorithm segmentation results,verify the superiority of the proposed algorithm.