娄联堂,汪然然.基于数字图像连续表示的图像分割方法[J].中南民族大学学报自然科学版,2022,41(3):374-378
基于数字图像连续表示的图像分割方法
Image segmentation method based on continuous representation of digital image
  
DOI:10.12130/znmdzk.20220317
中文关键词: 机器学习  图像分割  数字图像连续表示  梯度下降
英文关键词: machine learning  image segmentation  continuous representation of digital image  gradient descent
基金项目:国家自然科学基金资助项目(60975011)
作者单位
娄联堂 中南民族大学 数学与统计学学院武汉 430074 
汪然然 中南民族大学 数学与统计学学院武汉 430074 
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中文摘要:
      研究了一种基于数字图像连续表示的图像分割方法.首先根据机器学习模型的性质,将二维图像的分割问题转换为连续泛函的优化问题;其次利用数字图像的连续表示探讨连续泛函的数学表达式,使其能够表示基于深度学习的图像分割过程;接着通过建立连续泛函的约束条件,将优化问题转化为线性方程组求解的问题;最后利用梯度下降求解方程组,以实现复合绝缘子憎水性图像的分割.
英文摘要:
      An image segmentation method based on continuous representation of digital image is studied. Firstly, according to the nature of the machine learning model, the segmentation problem of two-dimensional images is converted into a continuous functional optimization problem. Secondly, the continuous representation of the digital image is used to explore the mathematical expression of continuous functional, so that it can express the image segmentation process based on deep learning. Then by establishing continuous functional constraints, the optimization problem is transformed into a solving problem of linear equation system. Finally, the gradient descent is used to solve the equation system to realize the segmentation of the hydrophobic image of the composite insulator.
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