沈阳农业大学学报2018，Vol.49Issue(2)：242-249,8.DOI:10.3969/j.issn.1000-1700.2018.02.017
柑橘表面缺陷图像快速准确分割方法
Fast and Accurate Segmentation Method for Surface Defects of Citrus
摘要
Abstract
Citrus surface defects affects the quality of fruit and food safety, so the detection of citrus surface defects has a great significance for improving the quality and value of fruits. Local Binary Fitting (LBF) is a image segmentation model which based on Chan-Vese (CV) model. Because the traditional LBF model has high requirements on the initial contour line and poor anti-noise ability. This paper presents a new LBF model based on the original LBF model by adding a kernel function (Gaussian function) and linear level set method for the LBF model improving. In order to solve the problem of image segmentation on the common defects of citrus surface (insect pests, decay, anthrax, wounds), an improved LBF model was used to verify whether the improved LBF model effectively extract the four common defects of cit rus surface. The results showed that the improved LBF model could be quickly identify the surface defects of insect pests, decayed fruits, anthrax fruits and medicinal fruits. The result is great and can be obtained with the defect image level set evolutionary images as well. It has the advantages of fewer iterations, shorter segmentation time, insensitive to the initial contour position, more smooth and complete segmentation contour, and accurate recognition of defect boundaries, which effectively solves the problem of the traditional LBF model. The experimental results showed that the improved LBF model was suitable for the segmentation and extraction of four kinds of citrus surface defects, which is feasible, rapid and accurate, and also provide a reference for the identification of citrus surface defects and on-line detection of citrus.关键词
柑橘表面缺陷/柑橘图像分割/LBF模型/水平集Key words
citrus surface defects/citrus image segmentation/LBF model/level set分类
农业科技引用本文复制引用
白雪冰,宋恩来,李润佳,许景涛..柑橘表面缺陷图像快速准确分割方法[J].沈阳农业大学学报,2018,49(2):242-249,8.基金项目
黑龙江省自然科学基金项目(C201208) （C201208）
