基于最小风险贝叶斯决策的织物图像分割

Image segmentation based on the minimum risk Bayes decision

为了实现利用机器视觉技术进行织物图像检测,对织物图像的分割进行了研究。依据最小风险贝叶斯决策理论,提出了一种基于最小风险贝叶斯决策的图像分割方法。首先建立图像分割的最小风险贝叶斯决策模型,对灰度级类条件概率密度估计出其符合正态分布的数学期望和方差以及损失函数,再依据最小风险贝叶斯决策理论对图像中的每一像素点进行目标图像和非目标图像的类别判断,从而实现目标图像的提取。实验结果表明,该方法在图像分割中是一种实用和成功的方法。

For using machine vision technology to detect textile image, segmentauon way of textile researched. By the minimum risk Bayes decision theory, this paper develops a new way of image segmentation： establish the mathematics model of image segmentation; estimate the probability density of grey scales and figure out its math-expectation and square difference that accord with normal distribution and the loss function ; and judge the every pixel dot in the image according to the minimum risk Bayes decision theory and determine whether it is of target or non-target images, thereby realizing the extraction of the target image. The experimental results indicate that the method is efficient and practicable.