基于模糊数学的群体图像分类

Ziyi Fu, Weixing Wang, Bo Yang, Bing Cui
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引用次数: 0

摘要

由于群体图像质量变化很大,普通的群体圈定算法难以分割各种群体图像,因此,在分割图像之前,需要对图像进行分类。本文提出了一种特殊的群体图像分类方法,即根据群体图像的特征定义判断集、确定模糊判断矩阵、定义权值集,即:(1)群体密度;(2)群体面积百分比(群体面积与图像整体面积之比);(3)群体面积方差;(4)菌落与营养液的灰色对比。实验证明,该方法分类合理,可用于群体图像识别和图像预分割,也可推广到其他类似应用中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Colony image classification on fuzzy mathematics
Due to colony image quality variation very much, an ordinary colony delineation algorithm is difficult to segment all kinds of colony images, therefore, image classification is necessary before image segmentation. The developed special colony image classification method in this study is to use definition of Judgment Set, determination of Fuzzy Judgment Matrix, and defining weight Set based on colony image characteristics, which are: (1) colony density; (2) colony area percentage (the ratio between colony area and whole area of the image); (3) colony area variance; and (4) grey contrast between colony and nutrient fluid. Experiments prove that the studied method make the classification reasonable, it can be used for colony image recognition and image pre-segmentation, and can also be expanded into the other similar applications.
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