图像多阈值分割在图像压缩、图像分析和模式识别等很多领域具有重要应用,但是阈值数的自动选择一直是至今未解决的难题.为此,基于分块采样和遗传算法提出一种自动多阈值图像分割算法.首先将一幅图像看成是由像素值组成的总体,运用分块采样得到若干子样本；其次在每一个子样本中运用遗传算法来使样本的均值与方差比极大化；再基于获得的样本信息对阈值数目和阈值进行自动预测；最后利用一种确定性的算法对阈值数和阈值做进一步的优化.该算法无需事先考虑图像的纹理和分割数等先验信息,具有较高的易用性,其计算复杂性对图像阈值个数敏感性较低,且无需进行灰度直方图分析.在Berkeley图像分割数据集上的大量仿真实验结果表明,文中算法能获得较准确、快速和稳定的图像分割.%Multilevel thresholding is an important technique for image compression, image analysis and pattern recognition. However, it is a hard problem to determine the number of thresholds automatically. In this paper, a new multilevel thresholding method called as automatic multilevel thresholding algorithm for image segmentation based on block sampling and genetic algorithm (AMT-BSGA) is proposed on the basis of block sampling and genetic algorithm. The proposed method can automatically determine the appropriate number of thresholds and the proper threshold values. In AMT-BSGA, an image is treated as a group of individual pixels with the gray values. First, an image is evenly divided into several blocks, and a sample is drawn from each block. Then, genetic algorithm based optimization is applied to each sample to maximize the ratio of mean and variance of the sample. Based on the optimized samples, the number of thresholds and threshold values are preliminarily determined. Finally, a deterministic method is implemented to further optimize the number of thresholds and threshold values. AMT-BSGA can work without prior knowledge on other auxiliary information, such as contextual or textual properties, and the number of thresholds. It is low in computing complexity which is almost independent from the number of thresholds and can avoid the burden of analyzing histograms. AMT-BSGA can produce effective, efficient and smoother results, computing complexity which is almost independent from the number of thresholds and can avoid the burden of analyzing histograms. AMT-BSGA can produce effective, efficient and smoother results, which has been verified by extensive simulations on Berkeley datasets.
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