基于遗传算法的最大熵阈值的图像分割

图像阈值分割技术在图像分析和图像识别中具有重要的意义.最大熵方法具有很多优点,但同时也存在弱点:需要大量的运算时间,特别是在计算多阈值时.因此需要引入优化算法.文中将遗传算法用于最大熵阈值的图像分割方法中,分别对一维及二维阈值分割的情况进行讨论,并提出了一种基于改进型遗传算法的最大熵阈值图像分割方法. 通过对几幅经典图像的分割结果对比,表明了基于遗传算法的最大熵阈值的图像分割方法可以有效地提高最大熵图像分割的计算速度,提高图像处理的实时性.

Maximum Entropy Thresholding Lmage Segmentation Based on Genetic Algorithm

Image thresholding segmentation is of important significance for image analysis and recognition. The maximum entropy thresholding segmentation method has many advantages. However, it has its weakness: it needs a great deal of computational time, especially when computing multithreshold. So it needs to import optimization technique. The maximum entropy thresholding segmentation method is implemented using genetic algorithm. The 1-D and 2-D maximum entropy thresholding segmentation methods are discussed and an improved maximum entropy thresholding segmentation method with genetic algorithm is presented. Compared to the traditional methods by using some classical images, the experiment results show that the segmentation method can improve the speed of image segmentation greatly.