研究图像分割,针对从图像中提取用户要求的特征目标,最优阈值的选取是图像准确分割的关键技术.传统二维最大熵值算法的最优阈值采用穷举方式进行寻优,耗时长,分割效率较低,易产生误分割.为了提高图像分割效率和准确性,提出一种遗传算法的二维最大熵值图像分割方法.先对原始图像进行灰度转换,绘制出图像的二维直方图.根据二维直方图信息选取适当灰度值进行初始化,采用遗传算法的初始种群,通过遗传算法选择、交叉和变异操作搜索最优阈值,获得的最优阈值对图像进行分割.实验结果表明,与传统二维最大熵值的图像分割算法相比,方法不仅运算速度加快,提高了分割效率,而且图像分割精度也大大提高.%In the 2-d image segmentation algorithm of maximum entropy value, the optimum threshold selection of image segmentation is the key technique. Traditional 2-d maximum entropy image segmentation algorithms use exhaustive way to find the optimal threshold, which is time-consuming, low efficient, and easy to generate the false division. In order to improve the accuracy and efficiency of image segmentation, this paper puts forward a genetic algorithm of 2-d maximum entropy value for image segmentation. This method firstly carries out gray level transform of the original image and draws the 2-d histogram. Then, according to the 2-d histogram information, appropriate gray value is selected to be initialized, The initial population of genetic algorithm is desinod, and each individual is represented with a mo-dimensional vector. Through the operators of selection, crossover and mutation, the optimal thresholds are searched, wich finally is taken as the optimal threshold of image segmentation. Experimental results show that compared with the maximum entropy with traditional 2-d image segmentation algorithm, this method can improve the computation speed, efficiency, and image segmentation accuracy.
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