基于模糊C均值聚类的比色传感器阵列图像分割算法
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Image Segmentation Algorithm of Colorimetric Sensor Array Based on Fuzzy C-means Clustering
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    摘要:

    结合当前比色传感器阵列多样性、不稳定等特点,并针对当前现有的阵列图像分割算法中或者效率低,或者易受光照环境影响等现状,本文在模糊C均值聚类算法基础上,提出了一种图像分割算法.该算法首先通过HSI颜色空间下I分量在行、列投影实现图像网格划分,并结合局部阵列点图像的平滑直方图信息解决了FCM算法聚类条件初始化的难题.其次,为了提高阵列点图像分割结果的准确度,该算法通过目标函数引入了不同权重系数的H分量和I分量,实现了色彩信息的引入.通过图像分割效果测试,本文所提出的图像分割算法在所有阵列点图像分割中展示了96.54%的总体最优分割精度,可以有效、准确地实现比色传感器阵列图像的目标提取.

    Abstract:

    Combining with the characteristics of current colorimetric sensor array such as diversity, instability, etc., and aiming at the current situation of the existing array image segmentation algorithm, such as low efficiency or susceptible to illumination environment, etc., this study proposes an image segmentation algorithm based on the fuzzy C-means clustering algorithm. Firstly, this algorithm achieves the grid division of image by the projection of I component in row and column under the HSI color space, and solves the problem of the initialization of clustering condition of FCM algorithm by combining with the smooth histogram information of local array point images. Secondly, in order to improve the accuracy of the result of segmentation of image points, the algorithm introduces the H component and I component of different weight coefficients through the objective function to realize the introduction of color information. Through the test of the effect of image segmentation, the image segmentation algorithm proposed in this study shows the overall optimal segmentation precision of 96.54% in all the image segmentation of the array points, and can effectively and accurately realize the target extraction of the colorimetric sensor array image.

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刘晏明,易鑫,李超.基于模糊C均值聚类的比色传感器阵列图像分割算法.计算机系统应用,2019,28(6):110-117

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历史
  • 收稿日期:2018-11-27
  • 最后修改日期:2018-12-18
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  • 在线发布日期: 2019-05-28
  • 出版日期: 2019-06-15
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