由于水下环境复杂多变,造成目标与伪目标的高混合度,某种单一的分割方法通常不能提取出理想的目标区域,因此提出一种基于Blob分析和贝叶斯决策的水下目标提取方法.首先,利用改进的二维OTSU算法计算出最佳阈值,并根据该阈值对图像进行阈值分割,经过连通性分析得到闭合的初始分割区域；然后,采用7种Blob算子对闭合区域进行7维向量描述,并基于贝叶斯决策准则剔除伪目标区域；最后,利用数学形态学算子去除目标区域边界的毛刺和干扰,得到理想的目标区域.通过对水池实验抓取的水下图像进行处理,结果表明该方法能够准确、有效地提取出真目标区域.%As it is known that the underwater environment is quite complicated and changeable, as a result, targets and pseudo targets always have a high degree of mixing, and one single segmentation method usually could not abstract ideal target regions. Therefore, this paper proposed a new segmentation method based on Blob analysis and Bayesian design-making. Firstly, the optimistic thresholds were calculated by the improved OTSU algorithm, and then the image was segmented according to this threshold. Through analyzing the connectivity characters, closed contours of regions were achieved. Secondly, the connected regions were described using 7 dimensions of Blob operators and pseudo-target regions were eliminated based on Bayesian decision-making rules. Finally, burrs and disturbances were wiped off through the usage of mathematical morphology operators and ideal target regions were achieved. Through dealing with the images grabbed during the pool experiments using the above method, accuracy and efficiency of the method were verified and the real target regions were acquired.
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