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Foreign Fiber Image Segmentation Based on Maximum Entropy and Genetic Algorithm

机译:基于最大熵和遗传算法的异物图像分割

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In machine-vision-based systems for detecting foreign fibers, due to the background of the cotton layer has the absolute advantage in the whole image, while the foreign fiber only account for a very small part, and what’s more, the brightness and contrast of the image are all poor. Using the traditional image segmentation method, the segmentation results are very poor. By adopting the maximum entropy and genetic algorithm, the maximum entropy function was used as the fitness function of genetic algorithm. Through continuous optimization, the optimal segmentation threshold is determined. Experimental results prove that the image segmentation of this paper not only fast and accurate, but also has strong adaptability.
机译:在基于机器视觉的异物纤维检测系统中,由于棉层的背景在整个图像中具有绝对优势,而异物纤维仅占很小的一部分,而且,其亮度和对比度图像都很差。使用传统的图像分割方法,分割结果非常差。通过采用最大熵和遗传算法,将最大熵函数用作遗传算法的适应度函数。通过连续优化,确定最佳分割阈值。实验结果证明,本文的图像分割不仅快速准确,而且适应性强。

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