【摘要】 针对部分田间图像由于其背景复杂、光照不均匀等导致很难确定图像分割的最佳阈值问题,提出了一种基于结合遗传算法Otsu算法改进的图像分割方法。首先对采集的图像进行预处理,基于预处理图像通过改进遗传算法中的选择、交叉、变异三种方法以及基于Otsu优化个体适应度函数,实现了可以自动调整遗传控制参数,既确保了物种的多样性又加快其收敛速度,为Otsu图像分割提供了最佳阈值,最后经过图像形态学对图像进行填充。将改进遗传算法的Otsu算法与基于遗传算法+Otsu算法进行图像分割以及基于遗传算法+Ksw熵值图像分割进行了对比,发现该算法得到的阈值范围较为稳定,使得分割后的图像准确、清晰,对于后期进行作物株数的统计或者植株的覆盖度有一定的帮助。更多还原

【Abstract】 For some field images, it is difficult to determine the optimal threshold problem of image segmentation due to its complicated background and uneven illumination. This paper proposes an image segmentation method based on improved Otsu algorithm optimization and improved genetic algorithm. Firstly, the acquired images are pre-processed. Based on the preprocessed images, the genetic control parameters can be automatically adjusted by improving the three methods of selection,crossover and variation in the genetic algorithm and optimizing the individual fitness function based on Otsu, so as to ensure the diversity of species and accelerate its convergence speed. The optimal threshold is provided for the Otsu image segmentation, and finally the image is filled by image morphology. In the result of the discussion, the algorithm results are compared with the Genetic Algorithm Based on the Otsu Algorithm and the Image Segmentation Based on Genetic Algorithm and KSW Entropy. It is found that the threshold range obtained by the algorithm is stable, which makes the segmented image accurate and clear. It is helpful to calculate the number of crops or the coverage of plants in the later stage.更多还原